{"paper_id":"00df40ea-31ac-4eab-97cd-a32be64fce6e","body_text":"A comparative assessment of data-driven flood susceptibility mapping in Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A comparative assessment of data-driven flood susceptibility mapping in Nigeria Wilmer Fabian Montien Tique, Marta Sapena, Matthias Weigand, Sandro Groth, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6756403/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract Flood risk in West Africa, particularly Nigeria, has significantly increased over the past five decades due to changing hydrological conditions, insufficient mitigation measures, and limited adaptation efforts. This study addresses the need for accurate and high-resolution data to support effective disaster risk management. Leveraging open-access remote sensing and geospatial data, we trained machine learning models to produce 30-meter resolution flood susceptibility maps for Nigeria. We compared four Digital Elevation Models (DEMs) and four hydrological methods (D8, D-inf, FD8, and Rho8) to model water flow direction and accumulation. Additional flood-influencing factors, such as land cover, soil characteristics, and proximity to water bodies, were also incorporated. Three models were developed and evaluated: random forest (RF), binary logistic regression (LG), and linear discriminant analysis (LDA). Across all models, the highest accuracy was achieved using the Copernicus DEM in combination with the D8 and FD8 methods. Model performance was validated against a major flood event in 2022, demonstrating a strong predictive capability. To reconcile differences among model outputs, we created an ensemble map that consolidates their strengths while accounting for uncertainty. We also estimated the population exposed to flood risk and found that approximately 11 million people in Nigeria currently live in flood-prone areas. This approach offers valuable insights for stakeholders seeking to strengthen localized disaster risk management. We discuss study limitations and outline directions for future research. To promote transparency and reproducibility, we provide the scripts used to generate the flood susceptibility maps, along with our final output maps for Nigeria: https://figshare.com/s/dc2318c9884f57b22c0d . Flood susceptibility mapping flood risk exposure machine learning models hydrological modelling Nigeria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Floods are a socio-natural hazard impacting 1.81 billion people annually, causing an average of 68 billion euros in damage (Bevere and Remondi 2022 ; Rentschler et al. 2022 ), with significant socio-economic consequences. Climate change exacerbates flood risk by increasing the frequency and intensity of rainfall, and the exposure of people and assets (Caretta et al. 2022 ; Taubenböck et al. 2024 ). Its impacts are particularly severe in the Global South, even though these regions have contributed minimally to global warming compared to the Global North (Boas et al. 2023 ). Africa, for example, faces serious climate change impacts, with people displaced by disasters, conflicts, and unemployment (Ibrahim and Mensah 2022 ; Mustak 2022 ; Mast et al. 2024 ). In West Africa, flooding is driven by more intense rainfall (Nka et al. 2015 ), deforestation, poor land use planning, rapid urbanization, and unsustainable agriculture (Di Baldassarre et al. 2010 ). Between 1966 and 2022, 312 flood events were recorded in West Africa, affecting 25 million people (CRED 2023 ). The worst-hit countries included Nigeria, Niger, Ghana, Mali, Senegal, Burkina Faso, Benin, and Mauritania. In 2022 alone, around 8.5 million people were affected; over 517,000 buildings were destroyed, 3.2 million people displaced, thousands killed or injured, and 1.6 million hectares of agricultural land damaged, increasing food security risks (OCHA 2023b ). Understanding disaster risks is a key priority of the Sendai Framework for Disaster Risk Reduction. However, in West Africa, flood hazards are often underestimated due to a lack of sufficiently detailed data, preventing decision-makers from fully comprehending flood risks. This gap often leads to trade-offs and maladaptive policies (Adeloye and Rustum 2011 ). Addressing this requires more accurate, high-resolution, and timely data (United Nations and UNISDR 2015 ; Notti et al. 2018 ). In this context, remote sensing, Geographic Information Systems (GIS), and machine learning offer valuable tools for modelling flood risks. Flood susceptibility refers to the spatial probability of an area being prone to flooding, independent of temporal occurrence, triggers, or losses (Hervás and Bobrowsky 2009 ; Domínguez-Cuesta 2013 ), providing valuable insights for flood risk analysis, such as hazard identification (Shah and Ai 2024 ). Flood risk includes hazard, exposure, and vulnerability. While risk assessment often incorporates factors like precipitation, this study focuses on flood susceptibility (a time-invariant perspective), allowing the integration with variables such as population density, infrastructure, and vulnerability indices for a broader understanding of flood risk. There are two main approaches to flood susceptibility modelling. The first, knowledge-based methods, such as Multi-Criteria Decision-Making (Vojtek and Vojtekova 2019 ), rely on expert knowledge (Arvor et al. 2021 ). These methods are highly interpretable but less adaptable to incomplete data (Mudashiru et al. 2021 ; Pradhan et al. 2023 ). The second, data-driven methods, use machine learning models to predict susceptibility based on ground-truth data and flood-influencing factors (Arvor et al. 2021 ). These methods are objective, scalable and accurate, but rely on the quality of the reference data (Mishra and Prasad 2024 ). Common models include logistic regression, linear discriminant analysis (Luu et al. 2022 ; Özay and Orhan 2023 ), random forest (Pourghasemi et al. 2020 ; Islam et al. 2021 ), and convolutional neural network (Khosravi et al. 2020 ; Wang et al. 2020 ). Flood susceptibility depends on flood-influencing factors (i.e., environmental and geographical variables that affect the likelihood, severity, and extent of flooding). These factors can be proxied from remote sensing and include elevation, slope, aspect, curvatures, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance to streams and water bodies, vegetation indices, land cover, soil type, drainage, precipitation, and geology (Tehrany et al. 2013 ; Wang et al. 2015 ; Rahmati and Pourghasemi 2017 ; Samanta et al. 2018 ; Ren et al. 2024 ). Dung et al. ( 2022 ) identified additional factors such as the Sediment Transport Index (STI) (Chen et al. 2020 ), groundwater levels (Hammami et al. 2019 ), and Height Above Nearest Drainage (HAND) (Alves et al. 2024 ). The selection of factors depends on the study area, data availability, and expert judgement (Petrucci 2022 ). Many topographical and hydrological factors are derived from Digital Elevation Models (DEMs), making DEM quality crucial for reliable flood mapping (Wang 2015 ; Munawar et al. 2022 ). Previous studies have assessed the impact of global DEMs on flood modelling, highlighting variations in predictive accuracy. Xu et al. ( 2021 ) and Zandsalimi et al. ( 2024 ) found NASADEM at 30-meter resolution among the most accurate DEMs, while lower resolution improves flood extent and depth prediction. Conversely, Avand et al. ( 2022 ) identified the 12.5 meter ALOS PALSAR DEM as the most effective, suggesting that higher resolution does not always improve predictions. Zhu et al. ( 2024 ) developed a deep-learning-based super-resolution model using SRTM and Sentinel-2A data, significantly enhancing flood simulation accuracy. Several studies have compared machine learning methods for flood susceptibility mapping using specific DEMs. Tien Bui et al. ( 2019 ) found the evidential belief function most accurate with ASTER DEM (28-meter resolution). El-Haddad et al. ( 2021 ) identified boosted regression trees as the best model using AW3D30 DEM, while Seydi et al. ( 2022 ) reported that cascade forest performed best with NASA-SRTM DEM (30-meter resolution). Gharakhanlou and Perez ( 2023 ) demonstrated random forest’s effectiveness with ALOS PALSAR DEM (12.5-meter resolution), and Islam et al. ( 2023 ) highlighted a hybrid random forest-artificial neural network approach using a 10-meter ALOS PALSAR DEM. In West Africa, Nigeria has been the most affected by floods in recent decades and is projected to become one of the world’s most populous countries by 2050 (United Nations Department of Economic and Social Affairs, Population Division 2024 ). In combination with expected increases in rainfall risks (IPCC 2022 ), flood risk is anticipated to rise substantially (Taubenböck et al. 2024 ). Accordingly, several studies have focused on flood susceptibility mapping in Nigeria. Nationally, Ighile, Shirakawa, and Tanikawa. (2022) used artificial neural networks and logistic regression. At the basin level, Komolafe et al. ( 2020 ), Alimi et al. ( 2022 ), and Nkeki et al. ( 2022 ) applied the Analytic Hierarchy Process (AHP), while Tella and Balogun. (2020) and Aladejana and Ebijuoworih. (2024) used fuzzy AHP, and Isiaka et al. ( 2023 ) used frequency ratio and GIS. At the urban scale, Idrees et al. ( 2022 ) adopted AHP. These studies consistently highlighted rainfall, proximity to streams, soil drainage, elevation, and slope as key factors. However, as noted by Komolafe et al. ( 2015 ), Tella and Balogun. (2020), Oloruntoba, Taiwo, and Agbogun. (2023), and Chinedu et al. ( 2024 ), further research is needed to evaluate different hydrological and machine learning approaches and to include a broader range of factors for more comprehensive flood susceptibility mapping in Nigeria. Despite recent advances, significant knowledge gaps remain regarding the most reliable datasets and models. In this context, this study pursues four main objectives: (1) to map flood susceptibility in Nigeria using remote sensing and geospatial data; (2) to assess the performance of various data-driven models (random forest, logistic regression, and linear discriminant analysis), and compare four openly available DEMs and four hydrological methods (D8, D-inf, FD8, and Rho8); (3) to validate the resulting maps against the 2022 flood event; and (4) to estimate the population exposed to flood risks. 2 Materials and Methods This section presents the study area and details the datasets used. We then outline the key flood-influencing factors and calculation methods, followed by the flood susceptibility modelling and evaluation approaches. Finally, we describe the estimation method for the exposed population (Fig. 1 ). 2.1 Study area This study focuses on Nigeria, given its demographic significance and extensive flood records over the past five decades. With a population of 218 million, Nigeria is the most populous country in Africa and is projected to reach 359 million by 2050 (United Nations Department of Economic and Social Affairs, Population Division 2024 ). It comprises sixteen states, with Abuja as its capital. Topographically, Nigeria is diverse, featuring plains, hills, plateaus, and mountains. The country experiences heavy rainfall from May to November and lies within the Niger and Benue river watersheds, with the Niger River flowing southward into the Gulf of Guinea (Echendu 2022 ). 2.2 Data We used several datasets for data-driven flood susceptibility mapping, including reference data and data to derive flood-influencing factors (Table 1 ). As reference data to train and validate our models, we employed the ‘maximum water extent’ dataset from the Global Surface Water Explorer (GSW), developed by the European Commission’s Joint Research Centre (JRC). This dataset identifies all locations detected as water over a 38-year period using Landsat imagery (Pekel et al. 2016 ), including permanent, seasonal, and one-time water pixels, as well as non-water pixels. We aggregated these into water and non-water categories, under the assumption that if a pixel has ever been flooded, it has a higher probability of being flooded again. To derive the geomorphometric factors (described in Section 2.3 ), we used four DEMs to compare their performance in modelling flood susceptibility in Nigeria: (1) the ALOS World 3D-30m Global Digital Surface Model (AW3D30) produced by JAXA, (2) the NASA SRTM Global 1 arc second V003 (SRTM) distributed by USGS, (3) the NASADEM Merged DEM Global 1 arc second V001 (NASADEM) developed by NASA, and (4) the Global Digital Elevation Model GLO-30 (GLO30) produced by Copernicus. These global DEMs are produced using radar interferometry and stereo photogrammetry and have a spatial resolution of 30 meters. We also used ancillary datasets representing lakes, coastline, land cover, soil types, soil drainage properties, and hydrogeology (Table 1 ). For exposure analysis, we used the Global Human Settlement Population (GHS-POP) data for 2020 to quantify population exposure. To evaluate the plausibility of our results, we used two remotely sensed datasets mapping flooded areas in Nigeria between September and October 2022. The first dataset, developed by DLR, maps flood extent using Sentinel-1 imagery, which can detect flooding even under cloudy conditions (Martinis et al. 2022 ). However, we identified false positives over airports and linear geometries in the overlap of Sentinel tiles. Following approaches proposed in the literature (Johnson et al. 2019 ; Huang et al. 2017 ; Hu and Demir 2021), we implemented the HAND method with different thresholds (2, 4, and 10 meters) to reduce artefacts in high-elevation areas away from streams. The second dataset, developed by the Global Flood Monitoring (GFM) system, consists of a binary flood mask derived from Sentinel-1 imagery using an ensemble of three algorithms to generate a more robust flood map (Salamon et al. 2021 ). Table 1 Datasets used in this study. Where: Global Surface Water Explored (GSW), Digital Elevation Model (DEM), ALOS World 3D-30m Digital Surface Model (AW3D30), NASA Shuttle Radar Topography Mission Global 1 arc second V003 (SRTM), NASADEM Merged DEM Global 1 arc second V001 (NASADEM), Global Digital Elevation Model GLO-30 (GLO30), and Global Human Settlement-Population (GHS-POP) Data Source Coverage Resolution/Scale Reference Maximum water extent GSW Global 30 meters (Pekel et al. 2016 ) DEM AW3D30 Global 30 meters (Tadono et al. 2014 ) DEM SRTM Global 30 meters (Land Processes Distributed Active Archive Center 2015 ) DEM NASADEM Global 30 meters (Buckley et al. 2020 ) DEM GLO30 Global 30 meters (Leister-Taylor et al. 2023 ) Lakes HydroLAKES v1.0 Global From 1:24,000 to 1:1 million* (Messager et al. 2016 ) (Lehner et al. 2022 ) Coastline African Marine Atlas Africa 1:250,000* (Scott et al. 2009 ) Land cover WorldCover V002 Global 10 meters (Tsendbazar et al. 2022 ) Soil atlas Soil Atlas of Africa Africa 1:3 million* (Jones et al. 2013 ) Soil drainage Soil property maps of Africa Africa 250 meters (Hengl et al. 2015) Hydrogeology Africa Groundwater Atlas Country Hydrogeology Maps, Version 1.0 Africa 1:5 million* (O Dochartaigh 2019 ) Population GHS-POP Global 100 meters (European Commission 2023 ) Flooded areas (Sept-Oct 2022) DLR Global 10 meters (Martinis et al. 2022 ) Flooded areas (Sept-Oct 2022) GFM Global 90 meters (European Commission. Joint Research Centre 2024) *Vector datasets where the scale is given instead of the spatial resolution. 2.3 Flood-influencing factors We calculated twenty-three flood-influencing factors to model flood susceptibility in Nigeria (Table 2 ). Only static factors were considered, as the objective was to model the spatial probability of flooding independent of external triggers like precipitation and soil moisture. To derive these factors, we used the R software (R Core Team 2022 ) and the “whitebox” package (Lindsay 2016a ). The topographic and hydrological factors listed in Table 2 were derived from a DEM. Given that DEMs often contain artefacts (e.g., spikes, holes, and artificial linear features), we pre-processed them using two noise-reduction algorithms: breaching depression least cost (Lindsay 2016b ; Lindsay and Dhun 2015 ), and depression filling (Wang and Liu 2006 ). This step ensured a cleaner surface by removing depressions and flattening areas. We then calculated the topographic and hydrological factors for each smoothed DEM (AW3D30, SRTM, NASADEM, and GLO30), resulting in four versions of each factor in Table 2 . We also derived flow direction and flow accumulation variables from the DEMs. Flow direction indicates the movement of water towards downstream areas, while flow accumulation measures the volume of water gathered from upstream areas. To calculate these, we applied four hydrological methods: D8 (O’Callaghan and Mark 1984 ) and Rho8 (Fairfield and Leymarie 1991 ) for single flow direction, and D-infinity (D-inf) (Tarboton 1997 ) and FD8 (Freeman 1991 ) for multiple flow direction. These flow variables are essential for calculating factors such as TWI, STI, SPI, HAND, Maximum Upslope Flow Path Length (MUFL), stream networks, and Downslope Distance to Stream (DDS). To compare the performance of the different methods and DEMs, we created versions of each factor corresponding to each DEM-hydrological method combination. Table 2 Description of the calculated flood-influencing factors. Terminology follows Lindsay ( 2023 ), and factors are grouped according to Kaya and Derin ( 2023 ). Distance-based factors are expressed in degrees. DEM = Digital Elevation Model Factors Description References Topographic 1 Elevation Altitude of the landscape (m). Wang et al. ( 2019 ) Slope Terrain steepness (degrees); influences water flow, saturation, erosion, sediment transport, and stream velocity. Janizadeh et al. ( 2019 ) Aspect Orientation of the slope. Yariyan et al. ( 2020 ) Profile curvature Rate of slope change downhill. Mirzaei et al. ( 2021 ) Tangential curvature Rate of aspect change along a contour line. Tripathi and Mohanty ( 2024 ) Topographic wetness index (TWI) Tendency of a location to become saturated based on contributing area and slope (dimensionless). Hitouri et al. ( 2024 ) Hydrological 2 Distance to main streams* Euclidean distance to high-order streams (e.g., order 6–7). Nguyen et al. ( 2022 ) Distance to secondary streams* Euclidean distance to low-order streams (e.g., order 1–3). Same as above Distance to all streams* Overall distance to streams regardless of order. Same as above Sediment transport index (STI) Potential for sheet erosion, based on flow accumulation and slope (dimensionless). Chen et al. ( 2020 ) Stream power index (SPI) Erosive energy of flowing water (dimensionless). Mabdeh et al. ( 2024 ) Height above nearest drainage (HAND) Vertical distance to the nearest stream (m). Rana et al. ( 2024 ) Downslope distance to stream (DDS) Horizontal flow-path distance to the nearest stream (m). Lindsay ( 2023 ) Maximum upslope flow path length (MUFL)* Maximum flow path length to the drainage divide (m). (D8 method only) Lindsay ( 2023 ) Land cover Distance to urban areas Euclidean distance to built-up areas. Habibi et al. ( 2023 ) Distance to trees Euclidean distance to tree-covered areas. Same as above Distance to vegetation Euclidean distance to shrubland, grassland, moss, and lichen areas. Same as above Distance to cropland and barren land Euclidean distance to agricultural and barren land. Same as above Distance to lakes Euclidean distance to lakes. Karlsson et al. ( 2017 ) Distance to coastline Euclidean distance to coastline. Hasan et al. ( 2023 ) Soil properties Soil type (FR weighted) Soil classification based on 39 soil types in Nigeria (Appendix A ). Bammou et al. ( 2024 ) Soil drainage class (FR weighted) Soil drainage classified according to FAO categories. Widya et al. ( 2024 ) Aquifer type (FR weighted) Classification of aquifers: surface water, sedimentary, igneous, basement rock. Allocca et al. ( 2021 ) Aquifer productivity (FR weighted) Aquifer productivity categorized into four classes. Kannapiran and Bhaskar ( 2024 ) Note: 1 Topographic factors are calculated four times, once for each DEM. 2 Hydrological factors are calculated sixteen times, once for each combination of DEM and hydrological method, except for distance to main streams, to secondary streams, to all streams, and MUFL, which are calculated only for a subset of hydrological methods. Once the factors in Table 2 were calculated, we post-processed and harmonized the data for spatial modelling. First, all factors were resampled to a 30-meter spatial resolution to match our reference data and reprojected to the World Geodetic System 1984 (WGS84) coordinate system. Second, because logistic regression and linear discriminant analysis models cannot directly process categorical variables (e.g., soil type, soil drainage class, and aquifer type and productivity), we transformed these into continuous variables using the Frequency Ratio (FR) method. FR is a bivariate statistical approach that assigns a weight coefficient to each category based on its probabilistic relationship with the dependent variable (Eq. 1 ), avoiding the creation of dummy variables (Sujatha and Sridhar 2021 ; Abdo et al. 2022 ). The FR is calculated for each category of each factor as follows (Eq. 1 ): $$\\:{FR}_{i,j}=\\frac{a}{A}/\\frac{b}{B},$$ 1 where: i , a category for a factor j , a is the number of flooded pixels for category i ; A the total number of flooded pixels in the study area; b the number of pixels for category i , and B the total number of pixels in the study area. The FR weight values for each category are presented in Appendix A . Finally, we conducted a correlation analysis to identify and exclude highly correlated factors. However, none of the factors exhibited high correlations (r > 0.85). Figure 2 illustrates the final set of flood-influencing factors. 2.4 Modelling flood susceptibility One of the objectives of this study is to compare the performance of data-driven methods for modelling flood susceptibility. For each subset of flood-influencing factors (corresponding to each DEM and hydrological method), we trained three models: a Random Forest (RF), a Logistic Regression (LG), and a Linear Discriminant Analysis (LDA). We created a random sample of 200,000 pixels using the ‘maximum water extent’ dataset. Half of the sample pixels correspond to flooded areas (covered by water at least once since 1985), and the other half to non-flooded areas (never covered by water since 1985). The sample was split into 70% for training and 30% for testing, to evaluate the model’s ability to predict unforeseen data. The models were fitted to perform a binary classification: “flooded” (1) and “non-flooded” (0), outputting continuous probability values of pixels belonging to the “flooded” class. To compare the different models, we used the following accuracy metrics: overall accuracy (OA), area under the curve (AUC) of the receiving operating characteristic (ROC) curve, precision, recall, and F1-score. Based on these metrics, we selected the DEM and hydrological method combination achieving the highest accuracy for RF, LG, and LDA. To assess the importance of flood-influencing factors, we used variable importance measures from each model. For RF, we employed the percent increase in mean square error (%IncMSE), where higher and positive values indicate greater variable importance (Breiman 2001 ). For LG, we evaluated the p-value and Estimate coefficient. The p-value reflects the statistical significance of the variable (p < 0.001 as highly significant, p < 0.01 as very significant, and p < 0.05 as significant), while the Estimate coefficient indicates the direction and strength of the relationship between the independent and the dependent variables, where positive values denote a direct relationship (Al-Juaidi et al. 2018 ). For LDA, we used group means to assess each variable’s contribution to class separability: a larger difference between the class means (flooded vs non-flooded) indicates a stronger contribution (Zhao et al. 2024 ). Furthermore, an analysis of variance (ANOVA) test was applied to the LDA model to evaluate the statistical significance of the variables (Kim 2017 ). 2.5 Evaluation of flood susceptibility maps Following the model validations, we evaluated the most accurate map produced by each method. We quantified the predictive capability of these maps against the extent of flooded areas from an actual event. For this, we used two datasets mapping flooded areas in Nigeria between September and October 2022 (Table 1 ). We re-classified the susceptibility maps into five categories: Very low [0-0.2], Low [0.2–0.4], Moderate [0.4–0.6], High [0.6–0.8], and Very High [0.8-1] following the approach of Meliho et al. ( 2021 ). By overlapping the two evaluation datasets with the three susceptibility maps, we quantified the total area of flooded pixels falling within each susceptibility category. Additionally, we combined the three best-performing maps (one from each model) to create a more accurate and reliable flood susceptibility map. This was done by calculating the mean susceptibility and standard deviation across the three maps. This ensemble approach mitigates biases and limitations inherent to each model, reduces the influence of outliers, and improves the overall reliability and accuracy of the final map (Hooftman et al. 2022 ). 2.6 Assessment of the exposed population We quantified the exposed population using the most accurate susceptibility map for each method, as well as the ensemble map, combined with population data per hectare for 2020. First, we resampled the population density data to a 30-meter spatial resolution and calculate the total population within a 30-meter pixel. We then overlaid these estimates with the reclassified susceptibility maps and aggregated the total population by susceptibility class. To estimate exposure, we defined high-susceptibility areas using a 0.5 threshold, based on the evaluation against the September-October 2022 flood event and reference data. Additionally, we aggregated the exposed population by administrative boundaries and by hexagon grid cells, using Uber’s discrete global hexagonal grid system (average hexagon area of 36.13 km 2 ) (Uber Technologies, Inc. 2024), to analyse intra-national variations and highlight specific areas of concern at a finer spatial scale. 3 Results 3.1 Validation and comparison of flood susceptibility models We generated forty-eight flood susceptibility maps by combining four DEMs (AW3D30, SRTM, NASADEM, and GLO30), four hydrological methods (D8, D-inf, FD8, and Rho8), and three models (RF, LG, and LDA). The GLO30/D8-RF map demonstrated the highest overall accuracy (0.9585), with strong performance across all evaluated metrics: AUC (0.9916), precision (0.9335), recall (0.9588), and F1-score (0.9460) (Table 3 ). These results highlight the model’s outstanding ability to distinguish flooded from non-flooded areas and identify flood-prone regions. For the logistic regression and linear discriminant analysis models, the GLO30/FD8 combination achieved the highest overall accuracy among statistical models (0.9215 and 0.91, respectively). These maps show similar performance patterns, albeit with slightly lower accuracies compared to the GLO30/D8-RF map. Table 3 Accuracy metrics for the flood susceptibility maps. The best-performing model within each DEM, based on each evaluation metric, is highlighted in bold DEM Method Model OA AUC Precision Recall F1-Score JAXA D8 RF 0.9558 0.9902 0.9311 0.9539 0.9424 LG 0.9154 0.9677 0.8873 0.8897 0.8885 LDA 0.9037 0.9625 0.8821 0.8608 0.8713 D-inf RF 0.9538 0.9896 0.9306 0.9490 0.9397 LG 0.9113 0.9636 0.8866 0.8781 0.8823 LDA 0.9015 0.9591 0.8823 0.8541 0.868 FD8 RF 0.9550 0.9901 0.9307 0.9522 0.9413 LG 0.9168 0.9690 0.8875 0.8936 0.8905 LDA 0.9064 0.9641 0.8839 0.8667 0.8752 Rho8 RF 0.9532 0.9896 0.9303 0.9475 0.9389 LG 0.9113 0.9636 0.8867 0.8780 0.8824 LDA 0.9015 0.9590 0.8822 0.8540 0.8679 USGS D8 RF 0.9532 0.9896 0.9276 0.9507 0.9390 LG 0.9147 0.9675 0.8903 0.8838 0.8870 LDA 0.9043 0.9624 0.8836 0.8608 0.8721 D-inf RF 0.9504 0.9889 0.9274 0.9428 0.9350 LG 0.9101 0.9636 0.8868 0.8744 0.8806 LDA 0.9027 0.9591 0.8839 0.8558 0.8696 FD8 RF 0.9526 0.9894 0.9282 0.9482 0.9381 LG 0.9162 0.9692 0.8903 0.8884 0.8893 LDA 0.9060 0.9644 0.8858 0.8633 0.8744 Rho8 RF 0.9502 0.9887 0.9270 0.9427 0.9348 LG 0.9102 0.9637 0.8868 0.8745 0.8806 LDA 0.9029 0.9591 0.8840 0.8560 0.8698 NASA D8 RF 0.9526 0.9894 0.9271 0.9497 0.9383 LG 0.9161 0.9679 0.8899 0.8886 0.8893 LDA 0.9048 0.9626 0.8838 0.8620 0.8728 D-inf RF 0.9503 0.9886 0.9275 0.9426 0.9350 LG 0.9111 0.9638 0.8877 0.8763 0.8819 LDA 0.9026 0.9591 0.8839 0.8552 0.8693 FD8 RF 0.9520 0.9893 0.9283 0.9466 0.9373 LG 0.9172 0.9689 0.8898 0.8918 0.8908 LDA 0.9056 0.9642 0.8844 0.8637 0.8739 Rho8 RF 0.9502 0.9886 0.9273 0.9424 0.9348 LG 0.9113 0.9637 0.8878 0.8766 0.8822 LDA 0.9025 0.9591 0.8837 0.8551 0.8692 GLO30 D8 RF 0.9585 0.9916 0.9335 0.9588 0.9460 LG 0.9202 0.9696 0.8929 0.8969 0.8949 LDA 0.9070 0.9641 0.8826 0.8704 0.8764 D-inf RF 0.9548 0.9907 0.9315 0.9507 0.9410 LG 0.9155 0.9654 0.8909 0.8856 0.8882 LDA 0.9052 0.9612 0.8832 0.8641 0.8735 FD8 RF 0.9577 0.9914 0.9325 0.9578 0.9450 LG 0.9215 0.9712 0.8922 0.9020 0.8970 LDA 0.9100 0.9663 0.8857 0.8756 0.8806 Rho8 RF 0.9552 0.9907 0.9325 0.9507 0.9415 LG 0.9157 0.9654 0.8912 0.8857 0.8884 LDA 0.9052 0.9612 0.8833 0.8640 0.8735 We also assessed the relevance of flood-influencing factors across the models. The GLO30/D8-RF model showed positive %IncMSE values for all factors, confirming their importance as predictors. The most influential factors, with %IncMSE values above 50%, included distances to the main streams, lakes, coastline, trees, urban areas, cropland and shrubland, soil drainage class, HAND, and elevation. In contrast, slope, TWI, SPI, and STI had comparatively less influence on model performance (Fig. 3 a). For the GLO30/FD8-LG model, elevation, slope, tangential curvature, TWI, distances to shrubland, cropland, and coastline, soil type, soil drainage class, and aquifer type and productivity emerged as highly significant, demonstrating a direct proportional relationship with flood susceptibility. Additionally, profile curvature, HAND, distances to the main and secondary streams, urban areas, trees, and lakes were influential but showed an inverse relationship with flood susceptibility. Figure 3 b illustrates the log-odd influence of these significant factors, highlighting both the magnitude and direction of their relationship. For the GLO30/FD8-LDA model, TWI, DDS, STI, distances to urban, shrubland, and cropland, soil type, soil drainage class, SPI, aquifer type and productivity showed stronger effect on class separability between flooded and non-flooded areas. An ANOVA test confirmed that all the factors were statistically significant for the model. Consistently, the most important factors across the RF, LG, and LDA models include distances to main streams, lakes, coastline, trees, urban areas, cropland, and shrubland, along with soil drainage class, HAND, and elevation. Figure 4 Flood susceptibility map by method: (a) GLO30/D8-RF; (b) GLO30/FD8-LG; and (c) GLO30/FD8-LDA. Subfigures a 1 , b 1 , and c 1 show one critical area impacted by the 2022 floods, while a 2 , b 2 , and c 2 represent a second critical area, providing more detailed view of each model’s performance in predicting flooded areas, (1) and (2) display the flooded areas identified by the DLR and GFM datasets for the same regions and timeframe 3.2 Evaluation of flood susceptibility maps Figure 5 shows the flood susceptibility levels estimated by the three models and the ensemble model for flooded areas in Nigeria between September and October 2022. The GLO30/D8-RF model estimates that 61% of flooded areas, based on GFM and DLR-H2 flood masks, had high to very high susceptibility. When including moderate susceptibility (greater than 0.4), this proportion rises to over 77%. In contrast, the DLR, DLR-H4, and DLR-H10 flood masks indicate slightly lower percentages of high and very high susceptibility (58%), increasing to nearly 75% when moderate susceptibility is included. For very low susceptibility areas, the model estimates between 10% and 13% of the flooded areas, indicating that very few regions classified as very low susceptibility were actually flooded. This highlights the model’s strong performance in estimating susceptibility based on the September-October 2022 flood event. The GLO30/FD8-LG model estimates high to very high susceptibility in 65–67% of the observed flooded areas based on the DLR-H2 and GFM flood masks, and 63–64% for the DLR, DLR-H4, and DLR-10 masks. Including moderate susceptibility increases these figures to approximately 75–77%. Notably, this model identifies the lowest proportion of low and very low susceptibility areas (9–10%) among the three methods. This suggests that the GLO30/FD8-LG model provides the most reliable estimation for identifying areas less likely to flood. The GLO30/FD8-LDA model behaves differently. It estimates a disproportionately large share of both very high and very low susceptibility levels in flooded areas, with fewer areas showing gradual transitions between susceptibility classes. Although the model captures extensively high-susceptibility regions, it tends to underestimate susceptibility in areas that experienced flooding during the September-October 2022 event. This suggests that linear models like LDA struggle to capture the complex relationships between flooding and geophysical variables, thereby reducing prediction accuracy. During the evaluation, we observed distinct strengths across the three models. Validation results indicate that the RF model achieved the highest overall accuracy. However, when evaluated against the 2022 flood event, the LG model produced more accurate estimations, while the LDA model identified the most extensive high-susceptibility areas. To improve the robustness of the results, we developed an ensemble model that combines all three approaches by calculating the mean and standard deviation of the susceptibility maps (Fig. 6 ). In this ensemble map, areas with greater uncertainty are represented by higher standard deviation values (reddish colours), while lower values indicate more consistent and reliable estimates (bluish colours). Based on the ensemble map and the DLR-H2 and GFM masks, 63–65% of the observed floods correspond to high and very high flood susceptibility areas, increasing to 74–76% when moderate susceptibility is included. These percentages are slightly lower for the DLR, DLR-H4, and DLR-H10 masks. As for very low flood susceptibility areas, only 11–13% of the total flooded areas were underestimated. 3.3 Exposed population in Nigeria Based on the GHS-POP dataset and our flood susceptibility models, we quantify the population exposed to floods in Nigeria for the year 2020. According to the GLO30/D8-RF map, approximately 9 million people were exposed to flood-prone areas (Fig. 7 ), representing 4.21% of the total population. Spatially, the most exposed states were Anambra, Bayelsa, Borno, Delta, Jigawa, Kogi, Lagos, and Rivers, which together accounted for around 6.5 million people exposed (Table 4 ). The GLO30/FD8-LG map estimates the highest exposure, with approximately 13 million people (6.63% of the population) living in flood-susceptible areas (Fig. 7 ). Regionally, the most exposed states include Adamawa, Anambra, Bayelsa, Borno, Delta, Lagos, Rivers, and Sokoto, accounting for 10 million people exposed (Table 4 ). According to the GLO30/FD8-LDA map, approximately 11 million people (5.59%) were exposed to floods in 2020 (Fig. 7 ). The states with the highest exposure in this scenario were Anambra, Bayelsa, Borno, Delta, Kebbi, Kogi, Lagos, and Rivers (Table 4 ), comprising 9 million of the exposed population. Table 4 Population exposed by state and model for the 15 most affected states in Nigeria (based on the ensemble map). Values are shown in units of 10,000 State GLO30/D8-RF GLO30/FD8-LG GLO30/FD8-LDA Ensemble Rivers 159.70 196.70 219.49 204.77 Bayelsa 131.38 195.31 182.12 178.44 Delta 141.96 189.64 181.19 175.51 Borno 45.20 109.57 81.88 79.74 Lagos 60.12 103.80 83.20 75.69 Anambra 59.89 70.29 71.67 69.79 Kogi 27.76 41.05 39.37 38.02 Kebbi 21.30 47.19 46.53 33.83 Ondo 24.52 35.05 33.74 33.41 Adamawa 17.84 51.87 38.65 29.68 Sokoto 22.06 50.73 21.70 24.31 Jigawa 27.99 41.46 9.83 18.10 Taraba 10.90 19.40 23.67 15.79 Niger 11.66 15.19 13.85 11.10 Yobe 15.52 20.01 4.18 9.01 Table 5 Frequency Ratio (FR) values for the categorical variables soil type, soil drainage class, and aquifer type and productivity Name Abbreviation FR Soil type Acrisols-Haplic ACha 0.42 Alisols-Haplic ALha 0.19 Arenosols-Brunic ARbr 0.25 Arenosols-Hypoluvic ARwl 0.38 Cambisols-Eutric CMeu 0 Cambisols-Ferralic CMfl 1.15 Cambisols-Gleiyic CMgl 1.91 Cambisols-Vertic CMvr 0.03 Fluvisols-Undifferentiated FL 2.18 Fluvisols-Dystric FLdy 0.34 Fluvisols-Eutric FLeu 1.20 Fluvisols-Thionic FLti 2.29 Ferrasols-Haplic FRha 0.02 Ferrasols-Umbric FRum 0 Ferrasols-Xanthic FRxa 0.36 Gleysols-Undifferentiated GL 1.83 Gleysols-Dystric GLdy 1.93 Gleysols-Eutric GLeu 2.03 Gleysols-Umbric GLum 2.19 Leptosols-Lithic LPli 0.41 Luvisols-Undifferentiated LV 0.36 Luvisols-Chromic LVcr 0 Luvisols-Gleiyic LVgl 0.13 Lixisols-Haplic LXha 0.69 Lixisols-Plinthic LXpl 0.18 Nitisols-Dystric NTdy 0.32 Nitisols-Eutric NTeu 0.51 Nitisols-Umbric NTum 0.15 Phaeozems-Haplic PHha 0.10 Planosols-Solodic PLsc 1.96 Plinthosols-Petric PTpt 0.51 Plinthosols-Pisoplinthic PTpx 0.20 Regosols-Dystric RGdy 1.95 Regosols-Eutric RGeu 0.01 Sea S 2.63 Solonchaks-Haplic SCha 0.48 Vertisols-Haplic VRha 1.33 Vertisols-Pellic VRpe 1.08 Water Body WR 2.53 Soil drainage class Extremely Poor - 2.61 Very poor - 2.34 Poor - 2.02 Imperfect - 0.88 Moderate - 0.16 Well - 0.04 Somewhat excessive - 0 Excessive - 0 Aquifer type and productivity Basement-low to Moderate productivity B-LM 0.48 Consolidated Sedimentary Intergranular-low to Moderate productivity CSI-LM 0.75 Consolidated Sedimentary Intergranular-moderate to High productivity CSI-MH 0.58 Consolidated Sedimentary Intergranular/Fracture moderate to High productivity CSIF-MH 0.42 Igneous-low to Moderate productivity I-LM 0.22 Unconsolidated sedimentary-High productivity U-H 1.96 Unconsolidated sedimentary-High to very high productivity U-VH 1.59 Surface water n/a 2.49 Based on the ensemble flood susceptibility map, approximately 11 million people (8.87%) were identified as living in flood-prone areas in 2020. Figure 8 presents a heatmap showing the spatial distribution of flood-exposed population at fine scale. This map enhances intra-regional disparities by identifying highly populated areas that overlap with high flood susceptibility zones, offering a valuable tool for targeted flood risk management. 4 Discussion In this study, we mapped flood susceptibility in Nigeria using open Earth observation and ancillary geospatial data at a 30-meter spatial resolution. Our results demonstrate the potential of data-driven machine learning approaches to generate comprehensive flood susceptibility maps in a data-scarce context. While previous studies have focused on individual states in Nigeria (Komolafe et al. 2020 ; Tella and Balogun 2020 ; Alimi et al. 2022 ; Nkeki et al. 2022 ; Idrees et al. 2022 ; Isiaka et al. 2023 ; Aladejana and Ebijuoworih 2024 ; Chinedu et al. 2024 ), few have examined flood susceptibility at the national level (Ighile, Shirakawa, and Tanikawa 2022 ). Our findings are largely consistent with prior work, particularly in the identification of key flood-influencing factors, such as elevation, distance to streams, soil drainage, slope, land cover, and soil type, and in recognizing the most flood-exposed states (Rivers, Delta, Lagos, Kogi, and Anambra). However, this study addresses critical gaps by integrating multiple data sources, hydrological methods, and modelling approaches, and by incorporating additional flood-influencing factors (Komolafe et al. 2015 ; Oloruntoba, Taiwo, and Agbogun 2023 ; Chinedu et al. 2024 ). As a result, this study provides a high-resolution and open-access flood susceptibility map for Nigeria, along with a replicable workflow that can be applied to other African countries. In total, we produced 48 flood susceptibility maps by combining four DEMs (AW3D30, SRTM, NASADEM, and GLO30), four hydrological methods (D8, D-inf, FD8, and Rho8), and three models (RF, LG, and LDA). The combinations GLO30/D8-RF, GLO30/FD8-LG, and GLO30/FD8-LDA delivered the highest accuracies. The RF model showed the best performance in validation, consistent with previous studies (Farhadi and Najafzadeh 2021 ; Ha and Kang 2022 ; Ganjirad and Delavar 2023 ; Wahba et al. 2024 ), as it effectively handles complex, nonlinear input data, while maintaining computational efficiency. While LG is a simpler algorithm suited to binary classification and works well with non-categorical predictors, it generally showed slightly lower accuracy than RF, though it performed best during real-event evaluation. LDA, while useful for interpreting factor contributions and classifying flooded areas, is more limited in capturing nonlinear relationships (Youssef et al. 2022 ). Among the DEMs, GLO30 consistently outperformed others. This result aligns with prior findings (Huang and Yu 2024 ; Trevisani et al. 2023 ) and supports the recommendations to use GLO30 DEM for hydrological analyses in Nigeria. The hydrological methods D8 and FD8 also delivered higher model performance than D-inf and Rho8, reinforcing their effectiveness for simulating environmental conditions in flood susceptibility analysis (Lindsay 2023 ). These findings underline the significant influence of both DEM choice and hydrological method on model accuracy and interpretability. Our literature-based framework identified 23 flood-influencing factors, grouped into topography, hydrological, land cover, and soil/hydrogeological categories. All factors were statistically significant, with low inter-correlations, enhancing model robustness. Key contributing variables included distances to main streams, lakes, coastline, urban areas, trees, shrubland, and cropland, as well as HAND, elevation, and soil drainage class. These variables are hydrologically relevant: proximity to water bodies directly influences flood risk; HAND indicates water accumulation potential; elevation and soil drainage class affect permeability and runoff; and land cover affects water retention. Our results confirm the importance of these factors, in line with previous studies (Seydi et al. 2022 ; Ozdemir et al. 2023 ; Pradhan et al. 2023 ; Demissie et al. 2024 ; Hitouri et al. 2024 ; Rana et al. 2024 ; Widya et al. 2024 ), and support the inclusion of elements such as HAND, lakes and vegetation distances, as proposed by Dung et al. ( 2022 ). To evaluate model performance, we compared predicted susceptibility maps with the observed flood extent from the September-October 2022 event, using GFM and DLR datasets. All three models showed good agreement with these datasets. While GLO30/D8-RF had the highest overall accuracy, GLO30/FD8-LG was most effective at identifying actual flooded areas, capturing 67% of observed floods in high and very high susceptibility zones. The GLO30/FD8-LDA map, despite lower overall accuracy, also identified many flood-prone areas, though its limitations in modelling non-linear relationships likely reduced its effectiveness. To address model-specific biases and uncertainties, we generated an ensemble flood susceptibility map that integrates predictions from all three models. Based on this ensembled map, we estimated that nearly 11 million people in Nigeria—approximately 5.27% of the total population in 2020—live in flood-prone areas. Of these, about 2 million are located in very high flood susceptibility zones, and 5 million in high susceptibility zones. These figures are lower than the 24.7 million estimated by Halima and Hiroaki ( 2022 ), but comparisons should be made cautiously. Their study employed a global 100-year flood hazard map at 1 km resolution (Dottori et al. 2016 ), whereas our approach used high resolution (30m) national flood susceptibility map, focused on long-term spatial risk derived from physical and environmental factors. Our maps do not rely on specific flood return periods or hazard intensities. Moreover, different population datasets were used. Supporting the plausibility of our estimates, OCHA (2023a) reported that over 4.5 million people in Nigeria were affected by floods in 2022. Spatial analysis of exposure reveal that the most exposed states include Rivers, Bayelsa, Delta, Borno, Lagos, Anambra, Kogi, Kebbi, Ondo, Adamawa, Sokoto, Jigawa, Yobe, Niger, and Taraba. Southern and southwestern states face frequent flooding due to high rainfall, soil saturation, and rapid urban expansion, leading to impervious surfaces. In contrast, northern states are more susceptible to flash floods between July to September. These arid regions suffer from limited soil porosity, poor drainage, sparse vegetation, and seasonal droughts, all of which intensify runoff and flood risk (Acheampong 1990 ). These findings can support decision-makers in identifying and prioritizing areas for targeted mitigation efforts. By highlighting population hotspots within flood-prone regions, this study provides a foundation for spatially informed flood risk management and resource allocation, similar to how landslide susceptibility has been combined with exposure and cost analysis to guide early warning system deployment (Sapena et al. 2023 ). This study limitations. First, our reference dataset is derived from Landsat imagery, which introduces two potential sources of error. One is the inherent inaccuracy of the surface water map, particularly for seasonal water bodies (Pekel et al. 2016 ). The second is source of error is the 8-day revisit period of the Landsat constellation, which may miss short-lived floods and produce false positives. As a result, our maps may not fully capture flash flood patterns or ephemeral events. Third, the modelling process itself is influenced by the choice of datasets, DEMs, hydrological methods, and machine learning models. While we addressed this by generating a large number of model combinations and producing an ensemble map to reduce uncertainty, future studies may further explore the sensitivity of specific model components. Our work can serve as a reference for identifying reliable datasets and methods in Western Africa, but results should still be interpreted in light of underlying data and modelling assumptions. This study provides a high-resolution, nationwide flood susceptibility map for Nigeria, offering practical insights for disaster risk management. While it reflects long-term spatial risk and exposure rather than immediate flood hazard, it forms a critical base layer for more dynamic hazard mapping. When combined with variables such as rainfall, soil moisture, and climate projections, it can contribute to flood hazard models that predict event-based risks. In parallel, incorporating advanced spatiotemporal modelling techniques—such as LSTM networks for population exposure (Geiß et al. 2024 )—can improve future estimations by accounting for urban expansion and demographic change. This offers a promising direction for enhancing flood risk analysis in fast-growing regions. 5 Conclusions This study addressed the need for reliable and high-resolution flood susceptibility maps in Nigeria, where floods displace millions of people annually. By identifying critical areas of flood risk, our work provides a practical tool for stakeholders to prioritize mitigation measures and strengthen resilience in vulnerable communities. Our data-driven approach demonstrates the feasibility of leveraging open-access geospatial data and machine learning to map flood susceptibility in Africa. It offers significant scalability to other West African countries facing similar challenges. Among the inputs evaluated, the GLO30 DEM from Copernicus proved to be the most suitable for hydrological analysis, while the D8 and FD8 algorithms consistently delivered the best model performance. We also tested three machine learning models, all of which showed high predictive capacity, though with some variations depending on evaluation metrics and comparison with real flood events. To reconcile these differences and strengthen reliability, we introduced an ensemble map that combines the strengths of each model while accounting for uncertainty, an essential consideration in flood risk decision-making. Overall, this study contributes to a better understanding of flood-prone areas in Nigeria and advances the broader field of flood risk management in data limited contexts. By emphasizing reproducibility and uncertainty, our approach supports more accurate, informed, and context-sensitive disaster preparedness and policy interventions across West Africa. Declarations Code and data availability. The code, datasets, flood susceptibility maps, and exposed population maps are temporarily available at https://figshare.com/s/dc2318c9884f57b22c0d. We provide examples of datasets to execute the code and produce the flood susceptibility maps at a smaller scale. Competing interests. The contact author has declared that none of the authors has any competing interests. Funding. This study has been conducted as part of the project MIGRAWARE (Grant No. 01LG2082C), funded by the German Federal Ministry of Education and Research (BMBF) as part of the programme WASCAL WRAP 2.0. Author contributions. Conceptualization: MS, WFMT; methodology, data curation, formal analysis, investigation, visualization: MS, WFMT; software: MS, WFMT, MW; data validation: SG; supervision, funding acquisition: HT, CG; writing – original draft: WFMT, MS; writing – review and editing: MS, WFMT, MW, SG, HT, CG. All authors have read and agreed to the published version of the manuscript. Acknowledgements. We thank Florian Fichtner for his support in preparing the flood mask for the September-October event in Nigeria. References Abdo HG, Almohamad H, Al Dughairi AA, Ali SA, Parvin F, Elbeltagi A, Costache R, Mohammed S, Al-Mutiry M, Alsafadi K (2022) Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6756403\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":464242635,\"identity\":\"a78db2cd-03ff-4a61-9f42-f8a45cbb6d11\",\"order_by\":0,\"name\":\"Wilmer Fabian Montien Tique\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBAC9gYehgMQJmMDA0MFkGZmbsCrhecATAsbSMsZkBZGwloggA1kURvMOnxa2M8ePPBzxzY5c/nmxseF82qj+duBWn5UbMOthScv4WDvmdvGlm2MzcYztx3PnXGYsYGx58xtnFrsGXIMDvC23U7ccIyxTZp327HcBqAWZsY23Fp4+N8YHPwL0dL+m3fOsdz5BLVI5BgchtnCzNtQk7uBsJZ3CYdl224bGxxLbJbmOXYgdyNQy0F8fuHhzz388W3bbTmDw8cffuapqcudd/7wwQc/KnBrQQeHweQBotUDQR0pikfBKBgFo2CEAAC4AmCl0O9cKgAAAABJRU5ErkJggg==\",\"orcid\":\"https://orcid.org/0009-0000-1988-0616\",\"institution\":\"University of Wurzburg: Julius-Maximilians-Universitat Wurzburg\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Wilmer\",\"middleName\":\"Fabian Montien\",\"lastName\":\"Tique\",\"suffix\":\"\"},{\"id\":464242636,\"identity\":\"7ececf50-1fa8-4c8a-8160-99a044ffdfa4\",\"order_by\":1,\"name\":\"Marta Sapena\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Universitat de València: Universitat de Valencia\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Marta\",\"middleName\":\"\",\"lastName\":\"Sapena\",\"suffix\":\"\"},{\"id\":464242637,\"identity\":\"3fe4161c-028f-42c3-8e88-5de12b2f0adb\",\"order_by\":2,\"name\":\"Matthias Weigand\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"German Aerospace Center DLR Oberpfaffenhofen Site: Deutsches Zentrum fur Luft- und Raumfahrt DLR Standort Oberpfaffenhofen\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Matthias\",\"middleName\":\"\",\"lastName\":\"Weigand\",\"suffix\":\"\"},{\"id\":464242638,\"identity\":\"bf8dd517-33bc-4c2f-b3b2-14e8688dd1cf\",\"order_by\":3,\"name\":\"Sandro Groth\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"German Aerospace Center DLR Oberpfaffenhofen Site: Deutsches Zentrum fur Luft- und Raumfahrt DLR Standort Oberpfaffenhofen\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sandro\",\"middleName\":\"\",\"lastName\":\"Groth\",\"suffix\":\"\"},{\"id\":464242639,\"identity\":\"dbdc1105-30d2-4ebb-996e-76ed980966bb\",\"order_by\":4,\"name\":\"Christian Geiß\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-7961-8553\",\"institution\":\"German Aerospace Center DLR Oberpfaffenhofen Site: Deutsches Zentrum fur Luft- und Raumfahrt DLR Standort Oberpfaffenhofen\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Christian\",\"middleName\":\"\",\"lastName\":\"Geiß\",\"suffix\":\"\"},{\"id\":464242640,\"identity\":\"bccde08a-75b1-4449-b60b-d2f8f6ccd4a1\",\"order_by\":5,\"name\":\"Hannes Taubenböck\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"German Aerospace Center DLR Oberpfaffenhofen Site: Deutsches Zentrum fur Luft- und Raumfahrt DLR Standort Oberpfaffenhofen\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hannes\",\"middleName\":\"\",\"lastName\":\"Taubenböck\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-05-27 07:09:01\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6756403/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6756403/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s11069-025-07868-y\",\"type\":\"published\",\"date\":\"2026-02-11T15:58:31+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":83902655,\"identity\":\"25c85c09-d24e-418c-908b-347fafb09c82\",\"added_by\":\"auto\",\"created_at\":\"2025-06-04 09:45:04\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":596978,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eWorkflow of the flood susceptibility mapping. GFM stands for Global Flood Monitoring, and DLR means the flood mask produced by the DLR Water Processor\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6756403/v1/3f6c44d3fca6e56d202c8e72.png\"},{\"id\":83902664,\"identity\":\"32be8067-5c5f-436f-aaf8-33650ffedcdc\",\"added_by\":\"auto\",\"created_at\":\"2025-06-04 09:45:04\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":22580690,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eReference data (dependent variable) and flood-influencing factors (independent variables) used in the models. Example shown the GLO30 DEM and the D8 hydrological method. Distance-based factors were converted to meters. Soil type, soil drainage class, and aquifer type and productivity display the FR weight coefficients (see Appendix A). The TWI, STI, and SPI factors are dimensionless\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6756403/v1/7322e4ee23b59b3873f5f0fc.png\"},{\"id\":83902659,\"identity\":\"5ac80126-fcbb-4c81-993e-abdc0d00c161\",\"added_by\":\"auto\",\"created_at\":\"2025-06-04 09:45:04\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":708399,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eOutcomes from the best-performing flood susceptibility maps: (a) variable importance for the GLO30/D8-RF map; (b) Estimated coefficient scores for the most significant factors (GLO30/FD8-LG map)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6756403/v1/74fa9a212fdc531a3179ab1d.png\"},{\"id\":83902662,\"identity\":\"51233243-bb65-4f8f-913d-f826d92d2712\",\"added_by\":\"auto\",\"created_at\":\"2025-06-04 09:45:04\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":22214642,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlood susceptibility map by method: (a) GLO30/D8-RF; (b) GLO30/FD8-LG; and (c) GLO30/FD8-LDA. Subfigures a\\u003csub\\u003e1\\u003c/sub\\u003e, b\\u003csub\\u003e1\\u003c/sub\\u003e, and c\\u003csub\\u003e1\\u003c/sub\\u003e show one critical area impacted by the 2022 floods, while a\\u003csub\\u003e2\\u003c/sub\\u003e, b\\u003csub\\u003e2\\u003c/sub\\u003e, and c\\u003csub\\u003e2\\u003c/sub\\u003e represent a second critical area, providing more detailed view of each model’s performance in predicting flooded areas, (1) and (2) display the flooded areas identified by the DLR and GFM datasets for the same regions and timeframe\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6756403/v1/b68f50bd0e37bfb3deead569.png\"},{\"id\":83902663,\"identity\":\"3d1c0590-50f0-49c3-a911-7f4c2156079c\",\"added_by\":\"auto\",\"created_at\":\"2025-06-04 09:45:04\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":447637,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEvaluation of flood susceptibility maps using the September-October 2022 flood event. Flood extent is overlaid with flood susceptibility levels, and the corresponding percentages are reported. Flood masks include the DLR flood mask, DLR masks adjusted using the Height Above the Nearest Drainage (HAND) thresholds of 2, 4, and 10 meters (DLR-H2, DLR-H4, and DLR-H10), and the Global Flood Monitoring (GFM) mask. The evaluation also includes the ensemble flood susceptibility map\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6756403/v1/a805dd353a12573575f8bf7a.png\"},{\"id\":83902661,\"identity\":\"c5d45e63-fa13-441f-98ba-ed713bf42eb3\",\"added_by\":\"auto\",\"created_at\":\"2025-06-04 09:45:04\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":4927699,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEnsemble flood susceptibility map, combining the mean output of three models with their associated uncertainty, represented by the standard deviation (sd). Areas of high susceptibility and low uncertainty appear in intense blue, while areas with low susceptibility and high uncertainty appear in intense red. Dark purple highlights regions with both high susceptibility and high uncertainty. The bivariate legend categorizes the mean and sd into four classes. Mean: (0, 0.25], [0.25, 0.5), [0.5, 0.75), and [0.75, -1), sd: (0, 0.15], [0.15, 0.3), [0.3, 0.55), and [0.5, -0.7]. Two regions are magnified for detailed examples\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6756403/v1/6d877b433aa81571c5452f41.png\"},{\"id\":83902660,\"identity\":\"5859c087-a2d9-4c9d-a0c9-19172a9f23c0\",\"added_by\":\"auto\",\"created_at\":\"2025-06-04 09:45:04\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":281566,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEstimated annual exposed population in 2020 by susceptibility level (very high, high, and moderate) and model (GLO30/D8-RF, GLO30/FD8-LG, GLO30/FD8-LDA, and ensemble map)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6756403/v1/a82533f4777c7556a064f303.png\"},{\"id\":83902657,\"identity\":\"cde82977-7613-43cb-b4fb-64f417fcc288\",\"added_by\":\"auto\",\"created_at\":\"2025-06-04 09:45:04\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1845931,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHeatmap showing population exposure to floods in 2020, based on the ensemble map. Darker colours indicate hotspots where highly populated areas overlap with high flood susceptibility. Each hexagon represents an area of 36.13 km\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6756403/v1/eb219f533d156ab4eb23a719.png\"},{\"id\":102785379,\"identity\":\"7c791856-156c-4072-9d20-0e870e43421a\",\"added_by\":\"auto\",\"created_at\":\"2026-02-16 16:05:55\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":63171585,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6756403/v1/77618cd5-548f-410c-b503-cd059ba350a9.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"A comparative assessment of data-driven flood susceptibility mapping in Nigeria\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eFloods are a socio-natural hazard impacting 1.81\\u0026nbsp;billion people annually, causing an average of 68\\u0026nbsp;billion euros in damage (Bevere and Remondi \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Rentschler et al. \\u003cspan citationid=\\\"CR105\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), with significant socio-economic consequences. Climate change exacerbates flood risk by increasing the frequency and intensity of rainfall, and the exposure of people and assets (Caretta et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Taubenb\\u0026ouml;ck et al. \\u003cspan citationid=\\\"CR115\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Its impacts are particularly severe in the Global South, even though these regions have contributed minimally to global warming compared to the Global North (Boas et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Africa, for example, faces serious climate change impacts, with people displaced by disasters, conflicts, and unemployment (Ibrahim and Mensah \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Mustak \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Mast et al. \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In West Africa, flooding is driven by more intense rainfall (Nka et al. \\u003cspan citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), deforestation, poor land use planning, rapid urbanization, and unsustainable agriculture (Di Baldassarre et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Between 1966 and 2022, 312 flood events were recorded in West Africa, affecting 25\\u0026nbsp;million people (CRED \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The worst-hit countries included Nigeria, Niger, Ghana, Mali, Senegal, Burkina Faso, Benin, and Mauritania. In 2022 alone, around 8.5\\u0026nbsp;million people were affected; over 517,000 buildings were destroyed, 3.2\\u0026nbsp;million people displaced, thousands killed or injured, and 1.6\\u0026nbsp;million hectares of agricultural land damaged, increasing food security risks (OCHA \\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e2023b\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eUnderstanding disaster risks is a key priority of the Sendai Framework for Disaster Risk Reduction. However, in West Africa, flood hazards are often underestimated due to a lack of sufficiently detailed data, preventing decision-makers from fully comprehending flood risks. This gap often leads to trade-offs and maladaptive policies (Adeloye and Rustum \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Addressing this requires more accurate, high-resolution, and timely data (United Nations and UNISDR \\u003cspan citationid=\\\"CR123\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Notti et al. \\u003cspan citationid=\\\"CR88\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In this context, remote sensing, Geographic Information Systems (GIS), and machine learning offer valuable tools for modelling flood risks.\\u003c/p\\u003e \\u003cp\\u003eFlood susceptibility refers to the spatial probability of an area being prone to flooding, independent of temporal occurrence, triggers, or losses (Herv\\u0026aacute;s and Bobrowsky \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Dom\\u0026iacute;nguez-Cuesta \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), providing valuable insights for flood risk analysis, such as hazard identification (Shah and Ai \\u003cspan citationid=\\\"CR111\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Flood risk includes hazard, exposure, and vulnerability. While risk assessment often incorporates factors like precipitation, this study focuses on flood susceptibility (a time-invariant perspective), allowing the integration with variables such as population density, infrastructure, and vulnerability indices for a broader understanding of flood risk.\\u003c/p\\u003e \\u003cp\\u003eThere are two main approaches to flood susceptibility modelling. The first, knowledge-based methods, such as Multi-Criteria Decision-Making (Vojtek and Vojtekova \\u003cspan citationid=\\\"CR125\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), rely on expert knowledge (Arvor et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). These methods are highly interpretable but less adaptable to incomplete data (Mudashiru et al. \\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Pradhan et al. \\u003cspan citationid=\\\"CR100\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The second, data-driven methods, use machine learning models to predict susceptibility based on ground-truth data and flood-influencing factors (Arvor et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). These methods are objective, scalable and accurate, but rely on the quality of the reference data (Mishra and Prasad \\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Common models include logistic regression, linear discriminant analysis (Luu et al. \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; \\u0026Ouml;zay and Orhan \\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), random forest (Pourghasemi et al. \\u003cspan citationid=\\\"CR99\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Islam et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), and convolutional neural network (Khosravi et al. \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR129\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFlood susceptibility depends on flood-influencing factors (i.e., environmental and geographical variables that affect the likelihood, severity, and extent of flooding). These factors can be proxied from remote sensing and include elevation, slope, aspect, curvatures, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance to streams and water bodies, vegetation indices, land cover, soil type, drainage, precipitation, and geology (Tehrany et al. \\u003cspan citationid=\\\"CR116\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR131\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Rahmati and Pourghasemi \\u003cspan citationid=\\\"CR102\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Samanta et al. \\u003cspan citationid=\\\"CR107\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Ren et al. \\u003cspan citationid=\\\"CR104\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Dung et al. (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) identified additional factors such as the Sediment Transport Index (STI) (Chen et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), groundwater levels (Hammami et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), and Height Above Nearest Drainage (HAND) (Alves et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The selection of factors depends on the study area, data availability, and expert judgement (Petrucci \\u003cspan citationid=\\\"CR98\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Many topographical and hydrological factors are derived from Digital Elevation Models (DEMs), making DEM quality crucial for reliable flood mapping (Wang \\u003cspan citationid=\\\"CR128\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Munawar et al. \\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003ePrevious studies have assessed the impact of global DEMs on flood modelling, highlighting variations in predictive accuracy. Xu et al. (\\u003cspan citationid=\\\"CR133\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) and Zandsalimi et al. (\\u003cspan citationid=\\\"CR136\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) found NASADEM at 30-meter resolution among the most accurate DEMs, while lower resolution improves flood extent and depth prediction. Conversely, Avand et al. (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) identified the 12.5 meter ALOS PALSAR DEM as the most effective, suggesting that higher resolution does not always improve predictions. Zhu et al. (\\u003cspan citationid=\\\"CR138\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) developed a deep-learning-based super-resolution model using SRTM and Sentinel-2A data, significantly enhancing flood simulation accuracy.\\u003c/p\\u003e \\u003cp\\u003eSeveral studies have compared machine learning methods for flood susceptibility mapping using specific DEMs. Tien Bui et al. (\\u003cspan citationid=\\\"CR118\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) found the evidential belief function most accurate with ASTER DEM (28-meter resolution). El-Haddad et al. (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) identified boosted regression trees as the best model using AW3D30 DEM, while Seydi et al. (\\u003cspan citationid=\\\"CR110\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) reported that cascade forest performed best with NASA-SRTM DEM (30-meter resolution). Gharakhanlou and Perez (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) demonstrated random forest\\u0026rsquo;s effectiveness with ALOS PALSAR DEM (12.5-meter resolution), and Islam et al. (\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) highlighted a hybrid random forest-artificial neural network approach using a 10-meter ALOS PALSAR DEM.\\u003c/p\\u003e \\u003cp\\u003eIn West Africa, Nigeria has been the most affected by floods in recent decades and is projected to become one of the world\\u0026rsquo;s most populous countries by 2050 (United Nations Department of Economic and Social Affairs, Population Division \\u003cspan citationid=\\\"CR124\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). In combination with expected increases in rainfall risks (IPCC \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), flood risk is anticipated to rise substantially (Taubenb\\u0026ouml;ck et al. \\u003cspan citationid=\\\"CR115\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Accordingly, several studies have focused on flood susceptibility mapping in Nigeria. Nationally, Ighile, Shirakawa, and Tanikawa. (2022) used artificial neural networks and logistic regression. At the basin level, Komolafe et al. (\\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), Alimi et al. (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), and Nkeki et al. (\\u003cspan citationid=\\\"CR87\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) applied the Analytic Hierarchy Process (AHP), while Tella and Balogun. (2020) and Aladejana and Ebijuoworih. (2024) used fuzzy AHP, and Isiaka et al. (\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) used frequency ratio and GIS. At the urban scale, Idrees et al. (\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) adopted AHP. These studies consistently highlighted rainfall, proximity to streams, soil drainage, elevation, and slope as key factors. However, as noted by Komolafe et al. (\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), Tella and Balogun. (2020), Oloruntoba, Taiwo, and Agbogun. (2023), and Chinedu et al. (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), further research is needed to evaluate different hydrological and machine learning approaches and to include a broader range of factors for more comprehensive flood susceptibility mapping in Nigeria. Despite recent advances, significant knowledge gaps remain regarding the most reliable datasets and models.\\u003c/p\\u003e \\u003cp\\u003eIn this context, this study pursues four main objectives: (1) to map flood susceptibility in Nigeria using remote sensing and geospatial data; (2) to assess the performance of various data-driven models (random forest, logistic regression, and linear discriminant analysis), and compare four openly available DEMs and four hydrological methods (D8, D-inf, FD8, and Rho8); (3) to validate the resulting maps against the 2022 flood event; and (4) to estimate the population exposed to flood risks.\\u003c/p\\u003e\"},{\"header\":\"2 Materials and Methods\",\"content\":\"\\u003cp\\u003eThis section presents the study area and details the datasets used. We then outline the key flood-influencing factors and calculation methods, followed by the flood susceptibility modelling and evaluation approaches. Finally, we describe the estimation method for the exposed population (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Study area\\u003c/h2\\u003e \\u003cp\\u003eThis study focuses on Nigeria, given its demographic significance and extensive flood records over the past five decades. With a population of 218\\u0026nbsp;million, Nigeria is the most populous country in Africa and is projected to reach 359\\u0026nbsp;million by 2050 (United Nations Department of Economic and Social Affairs, Population Division \\u003cspan citationid=\\\"CR124\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). It comprises sixteen states, with Abuja as its capital. Topographically, Nigeria is diverse, featuring plains, hills, plateaus, and mountains. The country experiences heavy rainfall from May to November and lies within the Niger and Benue river watersheds, with the Niger River flowing southward into the Gulf of Guinea (Echendu \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Data\\u003c/h2\\u003e \\u003cp\\u003eWe used several datasets for data-driven flood susceptibility mapping, including reference data and data to derive flood-influencing factors (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). As reference data to train and validate our models, we employed the \\u0026lsquo;maximum water extent\\u0026rsquo; dataset from the Global Surface Water Explorer (GSW), developed by the European Commission\\u0026rsquo;s Joint Research Centre (JRC). This dataset identifies all locations detected as water over a 38-year period using Landsat imagery (Pekel et al. \\u003cspan citationid=\\\"CR97\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), including permanent, seasonal, and one-time water pixels, as well as non-water pixels. We aggregated these into water and non-water categories, under the assumption that if a pixel has ever been flooded, it has a higher probability of being flooded again.\\u003c/p\\u003e \\u003cp\\u003eTo derive the geomorphometric factors (described in Section \\u003cspan refid=\\\"Sec5\\\" class=\\\"InternalRef\\\"\\u003e2.3\\u003c/span\\u003e), we used four DEMs to compare their performance in modelling flood susceptibility in Nigeria: (1) the ALOS World 3D-30m Global Digital Surface Model (AW3D30) produced by JAXA, (2) the NASA SRTM Global 1 arc second V003 (SRTM) distributed by USGS, (3) the NASADEM Merged DEM Global 1 arc second V001 (NASADEM) developed by NASA, and (4) the Global Digital Elevation Model GLO-30 (GLO30) produced by Copernicus. These global DEMs are produced using radar interferometry and stereo photogrammetry and have a spatial resolution of 30 meters. We also used ancillary datasets representing lakes, coastline, land cover, soil types, soil drainage properties, and hydrogeology (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFor exposure analysis, we used the Global Human Settlement Population (GHS-POP) data for 2020 to quantify population exposure.\\u003c/p\\u003e \\u003cp\\u003eTo evaluate the plausibility of our results, we used two remotely sensed datasets mapping flooded areas in Nigeria between September and October 2022. The first dataset, developed by DLR, maps flood extent using Sentinel-1 imagery, which can detect flooding even under cloudy conditions (Martinis et al. \\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). However, we identified false positives over airports and linear geometries in the overlap of Sentinel tiles. Following approaches proposed in the literature (Johnson et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Huang et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Hu and Demir 2021), we implemented the HAND method with different thresholds (2, 4, and 10 meters) to reduce artefacts in high-elevation areas away from streams. The second dataset, developed by the Global Flood Monitoring (GFM) system, consists of a binary flood mask derived from Sentinel-1 imagery using an ensemble of three algorithms to generate a more robust flood map (Salamon et al. \\u003cspan citationid=\\\"CR106\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDatasets used in this study. Where: Global Surface Water Explored (GSW), Digital Elevation Model (DEM), ALOS World 3D-30m Digital Surface Model (AW3D30), NASA Shuttle Radar Topography Mission Global 1 arc second V003 (SRTM), NASADEM Merged DEM Global 1 arc second V001 (NASADEM), Global Digital Elevation Model GLO-30 (GLO30), and Global Human Settlement-Population (GHS-POP)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eData\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSource\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCoverage\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eResolution/Scale\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMaximum water extent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGSW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Pekel et al. \\u003cspan citationid=\\\"CR97\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDEM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAW3D30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Tadono et al. \\u003cspan citationid=\\\"CR113\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDEM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSRTM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Land Processes Distributed Active Archive Center \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDEM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNASADEM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Buckley et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDEM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGLO30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Leister-Taylor et al. \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLakes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHydroLAKES v1.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eFrom 1:24,000 to 1:1 million*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Messager et al. \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003e(Lehner et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCoastline\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAfrican Marine Atlas\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAfrica\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1:250,000*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Scott et al. \\u003cspan citationid=\\\"CR109\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLand cover\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWorldCover V002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Tsendbazar et al. \\u003cspan citationid=\\\"CR121\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoil atlas\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSoil Atlas of Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAfrica\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1:3 million*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Jones et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoil drainage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSoil property maps of Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAfrica\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e250 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Hengl et al. 2015)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHydrogeology\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAfrica Groundwater Atlas Country Hydrogeology Maps, Version 1.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAfrica\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1:5 million*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(O Dochartaigh \\u003cspan citationid=\\\"CR89\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePopulation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGHS-POP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e100 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(European Commission \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFlooded areas (Sept-Oct 2022)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDLR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(Martinis et al. \\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFlooded areas (Sept-Oct 2022)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGFM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGlobal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e90 meters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e(European Commission. Joint Research Centre 2024)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e*Vector datasets where the scale is given instead of the spatial resolution.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Flood-influencing factors\\u003c/h2\\u003e \\u003cp\\u003eWe calculated twenty-three flood-influencing factors to model flood susceptibility in Nigeria (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Only static factors were considered, as the objective was to model the spatial probability of flooding independent of external triggers like precipitation and soil moisture. To derive these factors, we used the R software (R Core Team \\u003cspan citationid=\\\"CR101\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) and the \\u0026ldquo;whitebox\\u0026rdquo; package (Lindsay \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2016a\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe topographic and hydrological factors listed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e were derived from a DEM. Given that DEMs often contain artefacts (e.g., spikes, holes, and artificial linear features), we pre-processed them using two noise-reduction algorithms: breaching depression least cost (Lindsay \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2016b\\u003c/span\\u003e; Lindsay and Dhun \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), and depression filling (Wang and Liu \\u003cspan citationid=\\\"CR127\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). This step ensured a cleaner surface by removing depressions and flattening areas. We then calculated the topographic and hydrological factors for each smoothed DEM (AW3D30, SRTM, NASADEM, and GLO30), resulting in four versions of each factor in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eWe also derived flow direction and flow accumulation variables from the DEMs. Flow direction indicates the movement of water towards downstream areas, while flow accumulation measures the volume of water gathered from upstream areas. To calculate these, we applied four hydrological methods: D8 (O\\u0026rsquo;Callaghan and Mark \\u003cspan citationid=\\\"CR90\\\" class=\\\"CitationRef\\\"\\u003e1984\\u003c/span\\u003e) and Rho8 (Fairfield and Leymarie \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e) for single flow direction, and D-infinity (D-inf) (Tarboton \\u003cspan citationid=\\\"CR114\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e) and FD8 (Freeman \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e) for multiple flow direction. These flow variables are essential for calculating factors such as TWI, STI, SPI, HAND, Maximum Upslope Flow Path Length (MUFL), stream networks, and Downslope Distance to Stream (DDS). To compare the performance of the different methods and DEMs, we created versions of each factor corresponding to each DEM-hydrological method combination.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDescription of the calculated flood-influencing factors. Terminology follows Lindsay (\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), and factors are grouped according to Kaya and Derin (\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Distance-based factors are expressed in degrees. DEM\\u0026thinsp;=\\u0026thinsp;Digital Elevation Model\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFactors\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDescription\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReferences\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTopographic\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eElevation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAltitude of the landscape (m).\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eWang et al. (\\u003cspan citationid=\\\"CR130\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSlope\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTerrain steepness (degrees); influences water flow, saturation, erosion, sediment transport, and stream velocity.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eJanizadeh et al. (\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAspect\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOrientation of the slope.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eYariyan et al. (\\u003cspan citationid=\\\"CR134\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eProfile curvature\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRate of slope change downhill.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMirzaei et al. (\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTangential curvature\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRate of aspect change along a contour line.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTripathi and Mohanty (\\u003cspan citationid=\\\"CR120\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTopographic wetness index (TWI)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTendency of a location to become saturated based on contributing area and slope (dimensionless).\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHitouri et al. (\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHydrological\\u003c/b\\u003e\\u003csup\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance to main streams*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEuclidean distance to high-order streams (e.g., order 6\\u0026ndash;7).\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNguyen et al. (\\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance to secondary streams*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEuclidean distance to low-order streams (e.g., order 1\\u0026ndash;3).\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSame as above\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance to all streams*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOverall distance to streams regardless of order.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSame as above\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSediment transport index (STI)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePotential for sheet erosion, based on flow accumulation and slope (dimensionless).\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eChen et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStream power index (SPI)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eErosive energy of flowing water (dimensionless).\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMabdeh et al. (\\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeight above nearest drainage (HAND)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVertical distance to the nearest stream (m).\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRana et al. (\\u003cspan citationid=\\\"CR103\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDownslope distance to stream (DDS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHorizontal flow-path distance to the nearest stream (m).\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLindsay (\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMaximum upslope flow path length (MUFL)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMaximum flow path length to the drainage divide (m). (D8 method only)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLindsay (\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLand cover\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance to urban areas\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEuclidean distance to built-up areas.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHabibi et al. (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance to trees\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEuclidean distance to tree-covered areas.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSame as above\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance to vegetation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEuclidean distance to shrubland, grassland, moss, and lichen areas.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSame as above\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance to cropland and barren land\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEuclidean distance to agricultural and barren land.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSame as above\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance to lakes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEuclidean distance to lakes.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKarlsson et al. (\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance to coastline\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEuclidean distance to coastline.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHasan et al. (\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSoil properties\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoil type (FR weighted)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSoil classification based on 39 soil types in Nigeria (Appendix \\u003cspan refid=\\\"Sec15\\\" class=\\\"InternalRef\\\"\\u003eA\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eBammou et al. (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoil drainage class (FR weighted)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSoil drainage classified according to FAO categories.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eWidya et al. (\\u003cspan citationid=\\\"CR132\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAquifer type (FR weighted)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eClassification of aquifers: surface water, sedimentary, igneous, basement rock.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAllocca et al. (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAquifer productivity (FR weighted)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAquifer productivity categorized into four classes.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKannapiran and Bhaskar (\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"3\\\"\\u003eNote: \\u003csup\\u003e1\\u003c/sup\\u003eTopographic factors are calculated four times, once for each DEM. \\u003csup\\u003e2\\u003c/sup\\u003eHydrological factors are calculated sixteen times, once for each combination of DEM and hydrological method, except for distance to main streams, to secondary streams, to all streams, and MUFL, which are calculated only for a subset of hydrological methods.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eOnce the factors in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e were calculated, we post-processed and harmonized the data for spatial modelling. First, all factors were resampled to a 30-meter spatial resolution to match our reference data and reprojected to the World Geodetic System 1984 (WGS84) coordinate system. Second, because logistic regression and linear discriminant analysis models cannot directly process categorical variables (e.g., soil type, soil drainage class, and aquifer type and productivity), we transformed these into continuous variables using the Frequency Ratio (FR) method. FR is a bivariate statistical approach that assigns a weight coefficient to each category based on its probabilistic relationship with the dependent variable (Eq.\\u0026nbsp;\\u003cspan refid=\\\"Equ1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), avoiding the creation of dummy variables (Sujatha and Sridhar \\u003cspan citationid=\\\"CR112\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Abdo et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe FR is calculated for each category of each factor as follows (Eq.\\u0026nbsp;\\u003cspan refid=\\\"Equ1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e):\\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{FR}_{i,j}=\\\\frac{a}{A}/\\\\frac{b}{B},$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e1\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere: \\u003cem\\u003ei\\u003c/em\\u003e, a category for a factor \\u003cem\\u003ej\\u003c/em\\u003e, \\u003cem\\u003ea\\u003c/em\\u003e is the number of flooded pixels for category \\u003cem\\u003ei\\u003c/em\\u003e; \\u003cem\\u003eA\\u003c/em\\u003e the total number of flooded pixels in the study area; \\u003cem\\u003eb\\u003c/em\\u003e the number of pixels for category \\u003cem\\u003ei\\u003c/em\\u003e, and \\u003cem\\u003eB\\u003c/em\\u003e the total number of pixels in the study area.\\u003c/p\\u003e \\u003cp\\u003eThe FR weight values for each category are presented in Appendix \\u003cspan refid=\\\"Sec15\\\" class=\\\"InternalRef\\\"\\u003eA\\u003c/span\\u003e. Finally, we conducted a correlation analysis to identify and exclude highly correlated factors. However, none of the factors exhibited high correlations (r\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.85). Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e illustrates the final set of flood-influencing factors.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Modelling flood susceptibility\\u003c/h2\\u003e \\u003cp\\u003eOne of the objectives of this study is to compare the performance of data-driven methods for modelling flood susceptibility. For each subset of flood-influencing factors (corresponding to each DEM and hydrological method), we trained three models: a Random Forest (RF), a Logistic Regression (LG), and a Linear Discriminant Analysis (LDA).\\u003c/p\\u003e \\u003cp\\u003eWe created a random sample of 200,000 pixels using the \\u0026lsquo;maximum water extent\\u0026rsquo; dataset. Half of the sample pixels correspond to flooded areas (covered by water at least once since 1985), and the other half to non-flooded areas (never covered by water since 1985). The sample was split into 70% for training and 30% for testing, to evaluate the model\\u0026rsquo;s ability to predict unforeseen data. The models were fitted to perform a binary classification: \\u0026ldquo;flooded\\u0026rdquo; (1) and \\u0026ldquo;non-flooded\\u0026rdquo; (0), outputting continuous probability values of pixels belonging to the \\u0026ldquo;flooded\\u0026rdquo; class.\\u003c/p\\u003e \\u003cp\\u003eTo compare the different models, we used the following accuracy metrics: overall accuracy (OA), area under the curve (AUC) of the receiving operating characteristic (ROC) curve, precision, recall, and F1-score. Based on these metrics, we selected the DEM and hydrological method combination achieving the highest accuracy for RF, LG, and LDA.\\u003c/p\\u003e \\u003cp\\u003eTo assess the importance of flood-influencing factors, we used variable importance measures from each model. For RF, we employed the percent increase in mean square error (%IncMSE), where higher and positive values indicate greater variable importance (Breiman \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e). For LG, we evaluated the p-value and Estimate coefficient. The p-value reflects the statistical significance of the variable (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 as highly significant, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01 as very significant, and p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 as significant), while the Estimate coefficient indicates the direction and strength of the relationship between the independent and the dependent variables, where positive values denote a direct relationship (Al-Juaidi et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). For LDA, we used group means to assess each variable\\u0026rsquo;s contribution to class separability: a larger difference between the class means (flooded vs non-flooded) indicates a stronger contribution (Zhao et al. \\u003cspan citationid=\\\"CR137\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Furthermore, an analysis of variance (ANOVA) test was applied to the LDA model to evaluate the statistical significance of the variables (Kim \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Evaluation of flood susceptibility maps\\u003c/h2\\u003e \\u003cp\\u003eFollowing the model validations, we evaluated the most accurate map produced by each method. We quantified the predictive capability of these maps against the extent of flooded areas from an actual event. For this, we used two datasets mapping flooded areas in Nigeria between September and October 2022 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eWe re-classified the susceptibility maps into five categories: Very low [0-0.2], Low [0.2\\u0026ndash;0.4], Moderate [0.4\\u0026ndash;0.6], High [0.6\\u0026ndash;0.8], and Very High [0.8-1] following the approach of Meliho et al. (\\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). By overlapping the two evaluation datasets with the three susceptibility maps, we quantified the total area of flooded pixels falling within each susceptibility category.\\u003c/p\\u003e \\u003cp\\u003eAdditionally, we combined the three best-performing maps (one from each model) to create a more accurate and reliable flood susceptibility map. This was done by calculating the mean susceptibility and standard deviation across the three maps. This ensemble approach mitigates biases and limitations inherent to each model, reduces the influence of outliers, and improves the overall reliability and accuracy of the final map (Hooftman et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Assessment of the exposed population\\u003c/h2\\u003e \\u003cp\\u003eWe quantified the exposed population using the most accurate susceptibility map for each method, as well as the ensemble map, combined with population data per hectare for 2020. First, we resampled the population density data to a 30-meter spatial resolution and calculate the total population within a 30-meter pixel. We then overlaid these estimates with the reclassified susceptibility maps and aggregated the total population by susceptibility class. To estimate exposure, we defined high-susceptibility areas using a 0.5 threshold, based on the evaluation against the September-October 2022 flood event and reference data. Additionally, we aggregated the exposed population by administrative boundaries and by hexagon grid cells, using Uber\\u0026rsquo;s discrete global hexagonal grid system (average hexagon area of 36.13 km\\u003csup\\u003e2\\u003c/sup\\u003e) (Uber Technologies, Inc. 2024), to analyse intra-national variations and highlight specific areas of concern at a finer spatial scale.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3 Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Validation and comparison of flood susceptibility models\\u003c/h2\\u003e \\u003cp\\u003eWe generated forty-eight flood susceptibility maps by combining four DEMs (AW3D30, SRTM, NASADEM, and GLO30), four hydrological methods (D8, D-inf, FD8, and Rho8), and three models (RF, LG, and LDA). The GLO30/D8-RF map demonstrated the highest overall accuracy (0.9585), with strong performance across all evaluated metrics: AUC (0.9916), precision (0.9335), recall (0.9588), and F1-score (0.9460) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). These results highlight the model\\u0026rsquo;s outstanding ability to distinguish flooded from non-flooded areas and identify flood-prone regions.\\u003c/p\\u003e \\u003cp\\u003eFor the logistic regression and linear discriminant analysis models, the GLO30/FD8 combination achieved the highest overall accuracy among statistical models (0.9215 and 0.91, respectively). These maps show similar performance patterns, albeit with slightly lower accuracies compared to the GLO30/D8-RF map.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAccuracy metrics for the flood susceptibility maps. The best-performing model within each DEM, based on each evaluation metric, is highlighted in bold\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" 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colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9538\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9896\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.9306\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.9490\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.9397\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9113\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd 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align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9894\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.9282\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.9482\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.9381\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9162\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9692\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.8903\\u003c/p\\u003e 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colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLDA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9029\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9591\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.8840\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.8560\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.8698\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"9\\\" rowspan=\\\"10\\\"\\u003e \\u003cp\\u003eNASA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e 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colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.8844\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.8637\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.8739\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eRho8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9502\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9886\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.9273\\u003c/p\\u003e \\u003c/td\\u003e 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align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.8822\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLDA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9025\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9591\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.8837\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.8551\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.8692\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e 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align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9548\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9907\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.9315\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.9507\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.9410\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9155\\u003c/p\\u003e \\u003c/td\\u003e 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\\u003cp\\u003e0.8832\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.8641\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.8735\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eFD8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9577\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9914\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.9325\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.9578\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.9450\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.9215\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.9712\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.8922\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.9020\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.8970\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLDA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.9100\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.9663\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.8857\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.8756\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.8806\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eRho8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9552\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9907\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.9325\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.9507\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.9415\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9157\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9654\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.8912\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.8857\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.8884\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLDA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9052\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9612\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.8833\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.8640\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.8735\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe also assessed the relevance of flood-influencing factors across the models. The GLO30/D8-RF model showed positive %IncMSE values for all factors, confirming their importance as predictors. The most influential factors, with %IncMSE values above 50%, included distances to the main streams, lakes, coastline, trees, urban areas, cropland and shrubland, soil drainage class, HAND, and elevation. In contrast, slope, TWI, SPI, and STI had comparatively less influence on model performance (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea). For the GLO30/FD8-LG model, elevation, slope, tangential curvature, TWI, distances to shrubland, cropland, and coastline, soil type, soil drainage class, and aquifer type and productivity emerged as highly significant, demonstrating a direct proportional relationship with flood susceptibility. Additionally, profile curvature, HAND, distances to the main and secondary streams, urban areas, trees, and lakes were influential but showed an inverse relationship with flood susceptibility. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb illustrates the log-odd influence of these significant factors, highlighting both the magnitude and direction of their relationship.\\u003c/p\\u003e \\u003cp\\u003eFor the GLO30/FD8-LDA model, TWI, DDS, STI, distances to urban, shrubland, and cropland, soil type, soil drainage class, SPI, aquifer type and productivity showed stronger effect on class separability between flooded and non-flooded areas. An ANOVA test confirmed that all the factors were statistically significant for the model.\\u003c/p\\u003e \\u003cp\\u003eConsistently, the most important factors across the RF, LG, and LDA models include distances to main streams, lakes, coastline, trees, urban areas, cropland, and shrubland, along with soil drainage class, HAND, and elevation.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e Flood susceptibility map by method: (a) GLO30/D8-RF; (b) GLO30/FD8-LG; and (c) GLO30/FD8-LDA. Subfigures a\\u003csub\\u003e1\\u003c/sub\\u003e, b\\u003csub\\u003e1\\u003c/sub\\u003e, and c\\u003csub\\u003e1\\u003c/sub\\u003e show one critical area impacted by the 2022 floods, while a\\u003csub\\u003e2\\u003c/sub\\u003e, b\\u003csub\\u003e2\\u003c/sub\\u003e, and c\\u003csub\\u003e2\\u003c/sub\\u003e represent a second critical area, providing more detailed view of each model\\u0026rsquo;s performance in predicting flooded areas, (1) and (2) display the flooded areas identified by the DLR and GFM datasets for the same regions and timeframe\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Evaluation of flood susceptibility maps\\u003c/h2\\u003e \\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e shows the flood susceptibility levels estimated by the three models and the ensemble model for flooded areas in Nigeria between September and October 2022.\\u003c/p\\u003e \\u003cp\\u003eThe GLO30/D8-RF model estimates that 61% of flooded areas, based on GFM and DLR-H2 flood masks, had high to very high susceptibility. When including moderate susceptibility (greater than 0.4), this proportion rises to over 77%. In contrast, the DLR, DLR-H4, and DLR-H10 flood masks indicate slightly lower percentages of high and very high susceptibility (58%), increasing to nearly 75% when moderate susceptibility is included. For very low susceptibility areas, the model estimates between 10% and 13% of the flooded areas, indicating that very few regions classified as very low susceptibility were actually flooded. This highlights the model\\u0026rsquo;s strong performance in estimating susceptibility based on the September-October 2022 flood event.\\u003c/p\\u003e \\u003cp\\u003eThe GLO30/FD8-LG model estimates high to very high susceptibility in 65\\u0026ndash;67% of the observed flooded areas based on the DLR-H2 and GFM flood masks, and 63\\u0026ndash;64% for the DLR, DLR-H4, and DLR-10 masks. Including moderate susceptibility increases these figures to approximately 75\\u0026ndash;77%. Notably, this model identifies the lowest proportion of low and very low susceptibility areas (9\\u0026ndash;10%) among the three methods. This suggests that the GLO30/FD8-LG model provides the most reliable estimation for identifying areas less likely to flood.\\u003c/p\\u003e \\u003cp\\u003eThe GLO30/FD8-LDA model behaves differently. It estimates a disproportionately large share of both very high and very low susceptibility levels in flooded areas, with fewer areas showing gradual transitions between susceptibility classes. Although the model captures extensively high-susceptibility regions, it tends to underestimate susceptibility in areas that experienced flooding during the September-October 2022 event. This suggests that linear models like LDA struggle to capture the complex relationships between flooding and geophysical variables, thereby reducing prediction accuracy.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eDuring the evaluation, we observed distinct strengths across the three models. Validation results indicate that the RF model achieved the highest overall accuracy. However, when evaluated against the 2022 flood event, the LG model produced more accurate estimations, while the LDA model identified the most extensive high-susceptibility areas. To improve the robustness of the results, we developed an ensemble model that combines all three approaches by calculating the mean and standard deviation of the susceptibility maps (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). In this ensemble map, areas with greater uncertainty are represented by higher standard deviation values (reddish colours), while lower values indicate more consistent and reliable estimates (bluish colours). Based on the ensemble map and the DLR-H2 and GFM masks, 63\\u0026ndash;65% of the observed floods correspond to high and very high flood susceptibility areas, increasing to 74\\u0026ndash;76% when moderate susceptibility is included. These percentages are slightly lower for the DLR, DLR-H4, and DLR-H10 masks. As for very low flood susceptibility areas, only 11\\u0026ndash;13% of the total flooded areas were underestimated.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Exposed population in Nigeria\\u003c/h2\\u003e \\u003cp\\u003eBased on the GHS-POP dataset and our flood susceptibility models, we quantify the population exposed to floods in Nigeria for the year 2020. According to the GLO30/D8-RF map, approximately 9\\u0026nbsp;million people were exposed to flood-prone areas (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e), representing 4.21% of the total population. Spatially, the most exposed states were Anambra, Bayelsa, Borno, Delta, Jigawa, Kogi, Lagos, and Rivers, which together accounted for around 6.5\\u0026nbsp;million people exposed (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe GLO30/FD8-LG map estimates the highest exposure, with approximately 13\\u0026nbsp;million people (6.63% of the population) living in flood-susceptible areas (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). Regionally, the most exposed states include Adamawa, Anambra, Bayelsa, Borno, Delta, Lagos, Rivers, and Sokoto, accounting for 10\\u0026nbsp;million people exposed (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAccording to the GLO30/FD8-LDA map, approximately 11\\u0026nbsp;million people (5.59%) were exposed to floods in 2020 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). The states with the highest exposure in this scenario were Anambra, Bayelsa, Borno, Delta, Kebbi, Kogi, Lagos, and Rivers (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), comprising 9\\u0026nbsp;million of the exposed population.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePopulation exposed by state and model for the 15 most affected states in Nigeria (based on the ensemble map). Values are shown in units of 10,000\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eState\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGLO30/D8-RF\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGLO30/FD8-LG\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGLO30/FD8-LDA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eEnsemble\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRivers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e159.70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e196.70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e219.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e204.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBayelsa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e131.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e195.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e182.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e178.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDelta\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e141.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e189.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e181.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e175.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBorno\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e45.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e109.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e81.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e79.74\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLagos\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e60.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e103.80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e83.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnambra\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e59.89\\u003c/p\\u003e \\u003c/td\\u003e 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align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNiger\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYobe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20.01\\u003c/p\\u003e \\u003c/td\\u003e 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colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFLeu\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFluvisols-Thionic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFLti\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFerrasols-Haplic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFRha\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFerrasols-Umbric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFRum\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFerrasols-Xanthic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFRxa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGleysols-Undifferentiated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGleysols-Dystric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGLdy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.93\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGleysols-Eutric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGLeu\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGleysols-Umbric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGLum\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLeptosols-Lithic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLPli\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLuvisols-Undifferentiated\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLuvisols-Chromic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLVcr\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLuvisols-Gleiyic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLVgl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLixisols-Haplic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLXha\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLixisols-Plinthic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLXpl\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNitisols-Dystric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNTdy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNitisols-Eutric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNTeu\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNitisols-Umbric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNTum\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePhaeozems-Haplic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePHha\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlanosols-Solodic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePLsc\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlinthosols-Petric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePTpt\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlinthosols-Pisoplinthic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePTpx\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRegosols-Dystric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRGdy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRegosols-Eutric\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRGeu\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSea\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSolonchaks-Haplic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSCha\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVertisols-Haplic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVRha\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVertisols-Pellic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVRpe\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWater Body\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSoil drainage class\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eExtremely Poor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVery poor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePoor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eImperfect\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModerate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWell\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSomewhat excessive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eExcessive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAquifer type and productivity\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBasement-low to Moderate productivity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eB-LM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eConsolidated Sedimentary Intergranular-low to Moderate productivity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCSI-LM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eConsolidated Sedimentary Intergranular-moderate to High productivity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCSI-MH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eConsolidated Sedimentary Intergranular/Fracture moderate to High productivity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCSIF-MH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIgneous-low to Moderate productivity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eI-LM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnconsolidated sedimentary-High productivity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eU-H\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnconsolidated sedimentary-High to very high productivity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eU-VH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSurface water\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003en/a\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eBased on the ensemble flood susceptibility map, approximately 11\\u0026nbsp;million people (8.87%) were identified as living in flood-prone areas in 2020. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e presents a heatmap showing the spatial distribution of flood-exposed population at fine scale. This map enhances intra-regional disparities by identifying highly populated areas that overlap with high flood susceptibility zones, offering a valuable tool for targeted flood risk management.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4 Discussion\",\"content\":\"\\u003cp\\u003eIn this study, we mapped flood susceptibility in Nigeria using open Earth observation and ancillary geospatial data at a 30-meter spatial resolution. Our results demonstrate the potential of data-driven machine learning approaches to generate comprehensive flood susceptibility maps in a data-scarce context. While previous studies have focused on individual states in Nigeria (Komolafe et al. \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Tella and Balogun \\u003cspan citationid=\\\"CR117\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Alimi et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Nkeki et al. \\u003cspan citationid=\\\"CR87\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Idrees et al. \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Isiaka et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Aladejana and Ebijuoworih \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Chinedu et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), few have examined flood susceptibility at the national level (Ighile, Shirakawa, and Tanikawa \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Our findings are largely consistent with prior work, particularly in the identification of key flood-influencing factors, such as elevation, distance to streams, soil drainage, slope, land cover, and soil type, and in recognizing the most flood-exposed states (Rivers, Delta, Lagos, Kogi, and Anambra). However, this study addresses critical gaps by integrating multiple data sources, hydrological methods, and modelling approaches, and by incorporating additional flood-influencing factors (Komolafe et al. \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Oloruntoba, Taiwo, and Agbogun \\u003cspan citationid=\\\"CR94\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Chinedu et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). As a result, this study provides a high-resolution and open-access flood susceptibility map for Nigeria, along with a replicable workflow that can be applied to other African countries.\\u003c/p\\u003e \\u003cp\\u003eIn total, we produced 48 flood susceptibility maps by combining four DEMs (AW3D30, SRTM, NASADEM, and GLO30), four hydrological methods (D8, D-inf, FD8, and Rho8), and three models (RF, LG, and LDA). The combinations GLO30/D8-RF, GLO30/FD8-LG, and GLO30/FD8-LDA delivered the highest accuracies. The RF model showed the best performance in validation, consistent with previous studies (Farhadi and Najafzadeh \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ha and Kang \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Ganjirad and Delavar \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Wahba et al. \\u003cspan citationid=\\\"CR126\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), as it effectively handles complex, nonlinear input data, while maintaining computational efficiency. While LG is a simpler algorithm suited to binary classification and works well with non-categorical predictors, it generally showed slightly lower accuracy than RF, though it performed best during real-event evaluation. LDA, while useful for interpreting factor contributions and classifying flooded areas, is more limited in capturing nonlinear relationships (Youssef et al. \\u003cspan citationid=\\\"CR135\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAmong the DEMs, GLO30 consistently outperformed others. This result aligns with prior findings (Huang and Yu \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Trevisani et al. \\u003cspan citationid=\\\"CR119\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) and supports the recommendations to use GLO30 DEM for hydrological analyses in Nigeria. The hydrological methods D8 and FD8 also delivered higher model performance than D-inf and Rho8, reinforcing their effectiveness for simulating environmental conditions in flood susceptibility analysis (Lindsay \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). These findings underline the significant influence of both DEM choice and hydrological method on model accuracy and interpretability.\\u003c/p\\u003e \\u003cp\\u003eOur literature-based framework identified 23 flood-influencing factors, grouped into topography, hydrological, land cover, and soil/hydrogeological categories. All factors were statistically significant, with low inter-correlations, enhancing model robustness. Key contributing variables included distances to main streams, lakes, coastline, urban areas, trees, shrubland, and cropland, as well as HAND, elevation, and soil drainage class. These variables are hydrologically relevant: proximity to water bodies directly influences flood risk; HAND indicates water accumulation potential; elevation and soil drainage class affect permeability and runoff; and land cover affects water retention. Our results confirm the importance of these factors, in line with previous studies (Seydi et al. \\u003cspan citationid=\\\"CR110\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Ozdemir et al. \\u003cspan citationid=\\\"CR96\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Pradhan et al. \\u003cspan citationid=\\\"CR100\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Demissie et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Hitouri et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Rana et al. \\u003cspan citationid=\\\"CR103\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Widya et al. \\u003cspan citationid=\\\"CR132\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), and support the inclusion of elements such as HAND, lakes and vegetation distances, as proposed by Dung et al. (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTo evaluate model performance, we compared predicted susceptibility maps with the observed flood extent from the September-October 2022 event, using GFM and DLR datasets. All three models showed good agreement with these datasets. While GLO30/D8-RF had the highest overall accuracy, GLO30/FD8-LG was most effective at identifying actual flooded areas, capturing 67% of observed floods in high and very high susceptibility zones. The GLO30/FD8-LDA map, despite lower overall accuracy, also identified many flood-prone areas, though its limitations in modelling non-linear relationships likely reduced its effectiveness. To address model-specific biases and uncertainties, we generated an ensemble flood susceptibility map that integrates predictions from all three models.\\u003c/p\\u003e \\u003cp\\u003eBased on this ensembled map, we estimated that nearly 11\\u0026nbsp;million people in Nigeria\\u0026mdash;approximately 5.27% of the total population in 2020\\u0026mdash;live in flood-prone areas. Of these, about 2\\u0026nbsp;million are located in very high flood susceptibility zones, and 5\\u0026nbsp;million in high susceptibility zones. These figures are lower than the 24.7\\u0026nbsp;million estimated by Halima and Hiroaki (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), but comparisons should be made cautiously. Their study employed a global 100-year flood hazard map at 1 km resolution (Dottori et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), whereas our approach used high resolution (30m) national flood susceptibility map, focused on long-term spatial risk derived from physical and environmental factors. Our maps do not rely on specific flood return periods or hazard intensities. Moreover, different population datasets were used. Supporting the plausibility of our estimates, OCHA (2023a) reported that over 4.5\\u0026nbsp;million people in Nigeria were affected by floods in 2022.\\u003c/p\\u003e \\u003cp\\u003eSpatial analysis of exposure reveal that the most exposed states include Rivers, Bayelsa, Delta, Borno, Lagos, Anambra, Kogi, Kebbi, Ondo, Adamawa, Sokoto, Jigawa, Yobe, Niger, and Taraba. Southern and southwestern states face frequent flooding due to high rainfall, soil saturation, and rapid urban expansion, leading to impervious surfaces. In contrast, northern states are more susceptible to flash floods between July to September. These arid regions suffer from limited soil porosity, poor drainage, sparse vegetation, and seasonal droughts, all of which intensify runoff and flood risk (Acheampong \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e1990\\u003c/span\\u003e). These findings can support decision-makers in identifying and prioritizing areas for targeted mitigation efforts. By highlighting population hotspots within flood-prone regions, this study provides a foundation for spatially informed flood risk management and resource allocation, similar to how landslide susceptibility has been combined with exposure and cost analysis to guide early warning system deployment (Sapena et al. \\u003cspan citationid=\\\"CR108\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThis study limitations. First, our reference dataset is derived from Landsat imagery, which introduces two potential sources of error. One is the inherent inaccuracy of the surface water map, particularly for seasonal water bodies (Pekel et al. \\u003cspan citationid=\\\"CR97\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The second is source of error is the 8-day revisit period of the Landsat constellation, which may miss short-lived floods and produce false positives. As a result, our maps may not fully capture flash flood patterns or ephemeral events. Third, the modelling process itself is influenced by the choice of datasets, DEMs, hydrological methods, and machine learning models. While we addressed this by generating a large number of model combinations and producing an ensemble map to reduce uncertainty, future studies may further explore the sensitivity of specific model components. Our work can serve as a reference for identifying reliable datasets and methods in Western Africa, but results should still be interpreted in light of underlying data and modelling assumptions.\\u003c/p\\u003e \\u003cp\\u003eThis study provides a high-resolution, nationwide flood susceptibility map for Nigeria, offering practical insights for disaster risk management. While it reflects long-term spatial risk and exposure rather than immediate flood hazard, it forms a critical base layer for more dynamic hazard mapping. When combined with variables such as rainfall, soil moisture, and climate projections, it can contribute to flood hazard models that predict event-based risks. In parallel, incorporating advanced spatiotemporal modelling techniques\\u0026mdash;such as LSTM networks for population exposure (Gei\\u0026szlig; et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e)\\u0026mdash;can improve future estimations by accounting for urban expansion and demographic change. This offers a promising direction for enhancing flood risk analysis in fast-growing regions.\\u003c/p\\u003e\"},{\"header\":\"5 Conclusions\",\"content\":\"\\u003cp\\u003eThis study addressed the need for reliable and high-resolution flood susceptibility maps in Nigeria, where floods displace millions of people annually. By identifying critical areas of flood risk, our work provides a practical tool for stakeholders to prioritize mitigation measures and strengthen resilience in vulnerable communities.\\u003c/p\\u003e \\u003cp\\u003eOur data-driven approach demonstrates the feasibility of leveraging open-access geospatial data and machine learning to map flood susceptibility in Africa. It offers significant scalability to other West African countries facing similar challenges. Among the inputs evaluated, the GLO30 DEM from Copernicus proved to be the most suitable for hydrological analysis, while the D8 and FD8 algorithms consistently delivered the best model performance.\\u003c/p\\u003e \\u003cp\\u003eWe also tested three machine learning models, all of which showed high predictive capacity, though with some variations depending on evaluation metrics and comparison with real flood events. To reconcile these differences and strengthen reliability, we introduced an ensemble map that combines the strengths of each model while accounting for uncertainty, an essential consideration in flood risk decision-making.\\u003c/p\\u003e \\u003cp\\u003eOverall, this study contributes to a better understanding of flood-prone areas in Nigeria and advances the broader field of flood risk management in data limited contexts. By emphasizing reproducibility and uncertainty, our approach supports more accurate, informed, and context-sensitive disaster preparedness and policy interventions across West Africa.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCode and data availability.\\u0026nbsp;\\u003c/strong\\u003eThe code, datasets, flood susceptibility maps, and exposed population maps are temporarily available at https://figshare.com/s/dc2318c9884f57b22c0d. We provide examples of datasets to execute the code and produce the flood susceptibility maps at a smaller scale.\\u003c/p\\u003e\\n\\u003cp\\u003e \\u003ch2\\u003eCompeting interests.\\u003c/h2\\u003e \\u003cp\\u003eThe contact author has declared that none of the authors has any competing interests.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding.\\u003c/h2\\u003e \\u003cp\\u003eThis study has been conducted as part of the project MIGRAWARE (Grant No. 01LG2082C), funded by the German Federal Ministry of Education and Research (BMBF) as part of the programme WASCAL WRAP 2.0.\\u003c/p\\u003e\\u003ch2\\u003eAuthor contributions.\\u003c/h2\\u003e \\u003cp\\u003eConceptualization: MS, WFMT; methodology, data curation, formal analysis, investigation, visualization: MS, WFMT; software: MS, WFMT, MW; data validation: SG; supervision, funding acquisition: HT, CG; writing \\u0026ndash; original draft: WFMT, MS; writing \\u0026ndash; review and editing: MS, WFMT, MW, SG, HT, CG. All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements.\\u003c/h2\\u003e \\u003cp\\u003eWe thank Florian Fichtner for his support in preparing the flood mask for the September-October event in Nigeria.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAbdo HG, Almohamad H, Al Dughairi AA, Ali SA, Parvin F, Elbeltagi A, Costache R, Mohammed S, Al-Mutiry M, Alsafadi K (2022) Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria. Geoscience Lett 9:1\\u0026ndash;24\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAcheampong PK (1990) Climatological drought in Nigeria, GeoJournal, 20, 209\\u0026ndash;219\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAdeloye AJ, Rustum R (2011) Lagos (Nigeria) flooding and influence of urban planning, Proceedings of the Institution of Civil Engineers-Urban Design and Planning, 164, 175\\u0026ndash;187\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAl-Juaidi AE, Nassar AM, Al-Juaidi OE (2018) Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arab J Geosci 11:765\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAladejana OO, Ebijuoworih EJ (2024) Flood risk assessment in Kogi State Nigeria through the integration of hazard and vulnerability factors. Discover Geoscience 2:1\\u0026ndash;25\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAlimi S, Andongma T, Ogungbade O, Senbore S, Alepa V, Akinlabi O, Olawale L, Muhammed Q (2022) Flood vulnerable zones mapping using geospatial techniques: Case study of Osogbo Metropolis, Nigeria, The Egyptian. J Remote Sens Space Sci 25:841\\u0026ndash;850\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAllocca V, Di Napoli M, Coda S, Carotenuto F, Calcaterra D, Di Martire D, De Vita P (2021) A novel methodology for Groundwater Flooding Susceptibility assessment through Machine Learning techniques in a mixed-land use aquifer. 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Nat Reviews Methods Primers 4:70\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhu Y, Burlando P, Tan PY, Gei\\u0026szlig; C, Fatichi S (2024) Improving Pluvial Flood Simulations with Multi-source DEM Super-Resolution, Natural Hazards and Earth System Sciences Discussions, 1\\u0026ndash;22, 2024\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"natural-hazards\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"nhaz\",\"sideBox\":\"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)\",\"snPcode\":\"11069\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11069/3\",\"title\":\"Natural Hazards\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Flood susceptibility mapping, flood risk exposure, machine learning models, hydrological modelling, Nigeria\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6756403/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6756403/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFlood risk in West Africa, particularly Nigeria, has significantly increased over the past five decades due to changing hydrological conditions, insufficient mitigation measures, and limited adaptation efforts. This study addresses the need for accurate and high-resolution data to support effective disaster risk management. Leveraging open-access remote sensing and geospatial data, we trained machine learning models to produce 30-meter resolution flood susceptibility maps for Nigeria. We compared four Digital Elevation Models (DEMs) and four hydrological methods (D8, D-inf, FD8, and Rho8) to model water flow direction and accumulation. Additional flood-influencing factors, such as land cover, soil characteristics, and proximity to water bodies, were also incorporated. Three models were developed and evaluated: random forest (RF), binary logistic regression (LG), and linear discriminant analysis (LDA). Across all models, the highest accuracy was achieved using the Copernicus DEM in combination with the D8 and FD8 methods. Model performance was validated against a major flood event in 2022, demonstrating a strong predictive capability. To reconcile differences among model outputs, we created an ensemble map that consolidates their strengths while accounting for uncertainty. We also estimated the population exposed to flood risk and found that approximately 11\\u0026nbsp;million people in Nigeria currently live in flood-prone areas. This approach offers valuable insights for stakeholders seeking to strengthen localized disaster risk management. We discuss study limitations and outline directions for future research. To promote transparency and reproducibility, we provide the scripts used to generate the flood susceptibility maps, along with our final output maps for Nigeria: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://figshare.com/s/dc2318c9884f57b22c0d\\u003c/span\\u003e\\u003cspan address=\\\"https://figshare.com/s/dc2318c9884f57b22c0d\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A comparative assessment of data-driven flood susceptibility mapping in Nigeria\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-06-04 09:44:59\",\"doi\":\"10.21203/rs.3.rs-6756403/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Major revisions\",\"date\":\"2025-07-21T20:04:45+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2025-05-30T17:03:44+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-05-30T14:48:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-05-27T10:34:21+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Natural Hazards\",\"date\":\"2025-05-27T03:08:30+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"natural-hazards\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"nhaz\",\"sideBox\":\"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)\",\"snPcode\":\"11069\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11069/3\",\"title\":\"Natural Hazards\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"4dc3f7f4-2a19-4c42-bee1-3b1c5b2f31d1\",\"owner\":[],\"postedDate\":\"June 4th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-16T16:02:49+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6756403\",\"link\":\"https://doi.org/10.1007/s11069-025-07868-y\",\"journal\":{\"identity\":\"natural-hazards\",\"isVorOnly\":false,\"title\":\"Natural Hazards\"},\"publishedOn\":\"2026-02-11 15:58:31\",\"publishedOnDateReadable\":\"February 11th, 2026\"},\"versionCreatedAt\":\"2025-06-04 09:44:59\",\"video\":\"\",\"vorDoi\":\"10.1007/s11069-025-07868-y\",\"vorDoiUrl\":\"https://doi.org/10.1007/s11069-025-07868-y\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6756403\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6756403\",\"identity\":\"rs-6756403\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}