Geospatial Analysis of Flood Susceptible Areas in Damaturu Central, Yobe State, Nigeria.

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'Shitu Shehu Usman, Nuhu Yerima Ngurnoma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3909114/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study focuses on flood susceptibility mapping in Damaturu, Yobe State, Nigeria, leveraging Geographic Information Systems (GIS) and remote sensing techniques. Damaturu is prone to recurring flood events, necessitating effective flood mitigation and risk assessment strategies. Through the integration of GIS and remote sensing data, this research develops a robust flood susceptibility model. The study incorporates various data sources, including digital elevation models, hydrological data, and land-use maps, to create a comprehensive spatial database. Remote sensing data obtained from satellite and aerial platforms facilitate land cover change detection, flood extent identification, and flood-related damage assessment. The flood susceptibility mapping process employs GIS-based techniques, such as Analytical Hierarchy Process (AHP) and Weight of Evidence (WoE), to analyze and integrate the datasets, ultimately generating flood susceptibility maps for the area. These maps offer essential insights into flood-prone regions, aiding in flood risk assessment, disaster preparedness, and the development of targeted flood management strategies. The research outcomes are invaluable for policymakers, urban planners, and emergency response teams, enabling informed decision-making and proactive flood mitigation measures. The integration of GIS and remote sensing technologies ensures a comprehensive and adaptable approach to combat the challenges posed by floods in Damaturu, enhancing the resilience of local communities to future flood events. Flood susceptibility mapping Damaturu Yobe State Geographic Information Systems (GIS) Remote Sensing Analytical Hierarchy Process (AHP) Weight of Evidence (WoE) flood risk assessment disaster preparedness flood management strategies resilience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Water is essential to life and has been the backbone to early human decisions on the best area to live and procreate (McNeill, 2000 ). Hence people adapt to living along coastlines riversides. In Egypt for example, 95% of the population lives and thrives along the Nile River, thus, defining the culture, tradition and businesses of the people around it (Hegazy, 2018 ). Damaturu was declared the capital of Yobe State when the state was created in 1991 by the then military administration of Ibrahim Badamasi Babangida (Ningi et al., 2015 ). The city serving as the administrative capital therefore, brought together people from many other parts of the state and it has since then undergone infrastructural and economic upgrade. This blossoming upgrade on the other hand leads to development in both planned and unplanned areas like wetlands and low-elevated areas which are susceptible to flooding and thus, exacerbating the effect of flooding over Damaturu Metropolis. Many other factors contributed to increase in flooding in Damaturu Metropolis, including improper planning, inadequate use of modern technology to assess, plan and thus mitigate flooding which will mitigate the impact of flooding (Usman, 2022 ). The health impacts of flooding are enormous, some of these are: causing acute stress, malaria, cholera, depression, anxiety, posttraumatic stress disorder (PSTD), damage to infrastructure, disruption of the existing health system and healthcare delivery services damage to water and sewage systems and disruption to existing public health care programs (Abbas & Mahboobeh, 2020 ). These impacts depend on nature and scale of the flood disaster, the environment it occurred in, existing mitigation and prevention measures and pre-disaster functionality of public systems, which is largely decrepit in the study area. The presence of environmental contaminants, evacuation procedures and the assisted mobilization can exacerbate or mitigate the impact of flooding and other disasters (Abdullahi & Sadiq, 2020 ). Throughout the world, rivers have served multiple human purposes including agriculture, domestic use, industrial use, food transportation etc. Human beings have been attracted to settle on floodplain and river banks since time immemorial because of the rich alluvial soils, access to water supplies and cheap sites in urban centers especially for low-income families (Goudie, 2013 ; Levee, 2012 ). However, people living along floodplains of both major and minor river channels are constantly being ravaged by the menace of floods. Thus, necessitating proper management of coastal/riverine areas. There are many methods that can be used to assess susceptibility of areas to flooding including ground surveying (Bureau of Meteorology, 2017 ), historical data analysis (Dottori et al., 2013 ; Wasko et al., 2015 ) and remote sensing (Lillesand et al., 2018 ; Khosravi et al., 2020 ). This research used Remote Sensing and GIS to assess flood susceptible areas in Damaturu Metropolis. 2 Materials and Methods 2.1 Study Area This study covers Damaturu Metropolis, the administrative capital of Yobe State in Northeastern Nigeria. Damaturu is located along the famous Kano – Maiduguri highway, it is located between latitude 11° 30’ to 12° 01’ North of the Equator and between longitude 11° 40’ to 12° 18’ East of the Greenwich Meridian. It is surrounded by the LGAs of Tarmuwa to the north, Fune to the east and Gujba to the south, all these in Yobe state. Damaturu is bounded to the east by Magumeri and Kaga LGAs of Yobe State. Figure 1 shows the map of the study area. 2.2 Data processing For the purpose of this research, Landsat 8 OLI/TIRS images for the year 2023 was obtained from United States Geological Survey’s (USGS) Earth Explorer website ( https://Earthexplorer.usgs.gov ), this image was used to obtain the land use land cover (LULC) distribution over the study area as LULC is one of the factors that drives flooding over an area. Supervised image classification was adopted to carry out the classification. The process involves composing bands 5 (Near Infrared), 4 (Red) and 3 (Green) using ArcGIS composite bands tool, this tool concatenate multiple bands into one to create a coloured raster which can be displayed over RGB, this image is then clipped to the study area. Image clipping is the art of cutting an image to a shape extent. This tool takes in a raster file to be clipped as input raster, a shapefile as extent and output raster specifying the location and type of the image to be produced as raster. In order to carryout supervised image classification, training sites were selected to identify areas with pre-defined LULC classes. These areas are water body, bareland, built up and vegetation. Digital Elevation Model (DEM) was another dataset obtained from the same USGS’ Earth explorer website as SRTM. This dataset was clipped to the study area to create the DEM of Damaturu Metropolis. Aspect and slope of the study area were also analysed. Weighted overlay analysis was used in GIS to compute flood susceptibility map of the study area. The weights are presented in Table 1 . The location of certain areas of interests were obtained using GPS and are presented in Table 2 . Table 1 Classes of parameters and their weights (Source: Khosravi et al., 2020 ) Parameters Class Rating Weights Proximity to River 0–2000 10 2.6 2001–4000 8 4001–5000 6 5001–6000 4 6001–8000+ 2 Slope 0–5 10 1.5 5–10 8 10–15 6 15–20 4 > 20 2 Elevation 0-200 10 1 200–400 8 400–600 6 600–800 4 > 800 2 Land use Water 10 0.4 Agriculture 8 Urban 6 Mixed 4 Forest 2 Table 2 Geographical location of areas within Damaturu Metropolis Name Longitude Latitude Sabon Fegi 11° 58' 11° 44' Federal Polytechnic Damaturu 11° 59' 11° 44' Yobe State Assembly 11° 59' 11° 44' State Secretariat 11° 59' 11° 44' Waziri Ibrahim 11° 59' 11° 45' Federal HIgh Court 11° 59' 11° 45' Pompomari 11° 58' 11° 45' Pompomari West 11° 58' 11° 45' Buhari Housing Estate 11° 57' 11° 45' Juma'at Mosque 11° 58' 11° 44' Government House 11° 57' 11° 44' Malari 11° 57' 11° 43' Abba Ibrahim 11° 56' 11° 43' Afghanistan 11° 55' 11° 44' Bayan Tasha 11° 57' 11° 44' Nayi Nawa 11° 57' 11° 44' Shagari 11° 57' 11° 45' Ali Marami 11° 56' 11° 45' Tijjani Zanna Zakariyya 11° 56' 11° 46' Ministry of Works 11° 57' 11° 46' Yobe State University 11° 56' 11° 40' Don Etebe 11° 59' 11° 43' 3 Results and Discussion The LULC map of Damaturu Metropolis (see Fig. 2 ) shows a concentration of built up at the centre of the metropolis, water bodies showing the flow path through which water passes through the metropolis lies from northeast to southeast of the metropolis. Given that, the image was obtained during rainy season (September, 2023), a significant amount of vegetation is observed. The DEM, Aspect, Slope and susceptibility map are shown in Figs. 4 to 7. The distribution of LULC over the study area is presented in Table 3 and Pie chart of the distribution is shown in Fig. 3. Flow direction and watershed are also shown in Figs. 8 and 9. Table 3 Distribution of LULC classes over Damaturu Class Area Percent Built Up 1837.29 10% Water Body 1216.49 6% Vegetation 11461.89 61% Bareland 4382.34 23% 18898.02 100% 4 Conclusion From the flood susceptibility map of Damaturu, Nayi nawa, Shagari, Pompomari, Federal High Court, Waziri Ibrahim are found to have fallen in highly susceptible areas, whereas Tijjani Zanna Zakariya, Ministry of Works, Malari, Sabon Pegi, Bayan Tasha, Don Etebe were found to be less susceptible and therefore suitable for human development. Thus, GIS and remote sensing have proven in this study to be a reliable system for mapping flood susceptibility model using weighted overlay analysis. 5 Recommendations In a world of consistent, continuous technological and human advancement, prevention and mitigation of devastating natural is possible through consistent monitoring of the environment and examining the drivers of those natural hazards. Flood susceptibility assessment should be integrated into all government infrastructural provisions to avoid development of houses on flood plains. Thus, ensuring the safety of people and their wellbeing. Declarations Ethical Statement: The authors declare no conflict of interest and have adhered to all applicable ethical guidelines. The data and methods used in the study are available upon request. The study was funded by the researchers. The authors thank the study participants and the local communities for their cooperation and support. The study results will be used to inform flood risk management strategies in Damaturu Central, Yobe State, Nigeria, with the aim of reducing the negative impacts of floods on communities and improving the overall resilience of the area. Author Contribution 1. 'Shitu Shehu Usman carried out the data collection, geospatial analysis and proof reading.2. Nuhu Yerima Ngurnoma write the manuscript for draft submission and presentation. References Abbas A. G. & Mahboobeh H. (2020). Application of Flood Hazard Potential Zoning by using AHP Algorithm. Civil Eng Res J 9(5): CERJ.MS.ID.555775 (2020). Abdullahi M. & Sadiq A. Y. (2020). An Analysis of the Flood-Prone Areas in Birnin Kebbi, Using Remote Sensing and GIS. Journal of Geography, Environment and Earth Science International 24(6): 77-85. DOI: 10.9734/JGEESI/2020/v24i630237 Bureau of Meteorology. (2017). Guidelines for flood hazard mapping and floodplain management in Australia. Dottori, F., Simoncelli, S., & Todini, E. (2013). Flood hazard assessment using historical data and GIS: A case study of the Po River, Italy. Natural Hazards and Earth System Sciences, 13, 1511-1522. Goudie, A.S. (2013). The human impact on the natural environment: Past, present, and future. Wiley-Blackwell. Hegazy, M. (2018). The Nile: Its people, history, and culture. American University in Cairo Press. Khosravi, K., Panahi, M., & Pourghasemi, H. R. (2020). Flood hazard assessment using remote sensing and machine learning: A review. Remote Sensing of Environment, 248, 111981. Levee, B. (2012). River cities: A history. University of Chicago Press. Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2018). Remote sensing for flood hazard assessment. Springer Nature. McNeill, J. R. (2000). Something new under the sun: An environmental history of the twentieth-century world. New York: W.W. Norton & Company. Ningi A., Talib A., Bint L., Paim H. j. & Gill S. (2015). Cultural Dynamics of Child Labour in Yobe State Nigeria. International Journal of Humanities and Social Science. 20. 71-79. 10.9790/0837-20567179. Ouma, Y. O., Tateishi, R., & Honda, Y. (2015). Flood hazard assessment using remote sensing and GIS: A review. Journal of Hydroinformatics, 17, 1198-1224. Usman S. (2022). Flood disconnects road linking Damaturu, Dapchi, other communities in Yobe. Daily Post Nigeria. https://dailypost.ng/2022/09/12/flood-disconnects-road-linking-damaturu-dapchi-other-communities-in-yobe/ Wasko, C., Nathan, R. J., & Seed, A. (2015). Flood hazard assessment using historical data: A case study of the Brisbane River, Australia. Journal of Hydrology, 524, 469-482. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-3909114","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":277056509,"identity":"28d0566b-fe74-4b21-8e7c-d3a4646f9f86","order_by":0,"name":"'Shitu Shehu 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6","display":"","copyAsset":false,"role":"figure","size":160049,"visible":true,"origin":"","legend":"\u003cp\u003eAspect\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3909114/v1/5f9539a382e30fc918941c46.jpg"},{"id":52447525,"identity":"0ff66c1c-172b-47c9-9786-3394c62fd531","added_by":"auto","created_at":"2024-03-11 18:30:59","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":106812,"visible":true,"origin":"","legend":"\u003cp\u003eFlood susceptibility map\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3909114/v1/f2f44311e9da9763969a5240.jpg"},{"id":52447005,"identity":"4d6a3cd9-9b1f-4b5e-8503-30063c7a2ee5","added_by":"auto","created_at":"2024-03-11 18:22:58","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":168281,"visible":true,"origin":"","legend":"\u003cp\u003eFlow direction\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3909114/v1/ee8d2a2966b29d854b85e502.jpg"},{"id":52447006,"identity":"944e9c2c-d798-42ef-9a94-0b3008b2c256","added_by":"auto","created_at":"2024-03-11 18:22:59","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":62099,"visible":true,"origin":"","legend":"\u003cp\u003eWatershed\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3909114/v1/35833025308902aee04919c2.jpg"},{"id":52509200,"identity":"eccf955b-3c94-4743-80d8-2c509490a033","added_by":"auto","created_at":"2024-03-12 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Hence people adapt to living along coastlines riversides. In Egypt for example, 95% of the population lives and thrives along the Nile River, thus, defining the culture, tradition and businesses of the people around it (Hegazy, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Damaturu was declared the capital of Yobe State when the state was created in 1991 by the then military administration of Ibrahim Badamasi Babangida (Ningi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The city serving as the administrative capital therefore, brought together people from many other parts of the state and it has since then undergone infrastructural and economic upgrade. This blossoming upgrade on the other hand leads to development in both planned and unplanned areas like wetlands and low-elevated areas which are susceptible to flooding and thus, exacerbating the effect of flooding over Damaturu Metropolis. Many other factors contributed to increase in flooding in Damaturu Metropolis, including improper planning, inadequate use of modern technology to assess, plan and thus mitigate flooding which will mitigate the impact of flooding (Usman, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe health impacts of flooding are enormous, some of these are: causing acute stress, malaria, cholera, depression, anxiety, posttraumatic stress disorder (PSTD), damage to infrastructure, disruption of the existing health system and healthcare delivery services damage to water and sewage systems and disruption to existing public health care programs (Abbas \u0026amp; Mahboobeh, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These impacts depend on nature and scale of the flood disaster, the environment it occurred in, existing mitigation and prevention measures and pre-disaster functionality of public systems, which is largely decrepit in the study area. The presence of environmental contaminants, evacuation procedures and the assisted mobilization can exacerbate or mitigate the impact of flooding and other disasters (Abdullahi \u0026amp; Sadiq, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThroughout the world, rivers have served multiple human purposes including agriculture, domestic use, industrial use, food transportation etc. Human beings have been attracted to settle on floodplain and river banks since time immemorial because of the rich alluvial soils, access to water supplies and cheap sites in urban centers especially for low-income families (Goudie, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Levee, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, people living along floodplains of both major and minor river channels are constantly being ravaged by the menace of floods. Thus, necessitating proper management of coastal/riverine areas.\u003c/p\u003e \u003cp\u003eThere are many methods that can be used to assess susceptibility of areas to flooding including ground surveying (Bureau of Meteorology, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), historical data analysis (Dottori et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wasko et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and remote sensing (Lillesand et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Khosravi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This research used Remote Sensing and GIS to assess flood susceptible areas in Damaturu Metropolis.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThis study covers Damaturu Metropolis, the administrative capital of Yobe State in Northeastern Nigeria. Damaturu is located along the famous Kano \u0026ndash; Maiduguri highway, it is located between latitude 11\u0026deg; 30\u0026rsquo; to 12\u0026deg; 01\u0026rsquo; North of the Equator and between longitude 11\u0026deg; 40\u0026rsquo; to 12\u0026deg; 18\u0026rsquo; East of the Greenwich Meridian. It is surrounded by the LGAs of Tarmuwa to the north, Fune to the east and Gujba to the south, all these in Yobe state. Damaturu is bounded to the east by Magumeri and Kaga LGAs of Yobe State. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the map of the study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data processing\u003c/h2\u003e \u003cp\u003eFor the purpose of this research, Landsat 8 OLI/TIRS images for the year 2023 was obtained from United States Geological Survey\u0026rsquo;s (USGS) Earth Explorer website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://Earthexplorer.usgs.gov\u003c/span\u003e\u003cspan address=\"https://Earthexplorer.usgs.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), this image was used to obtain the land use land cover (LULC) distribution over the study area as LULC is one of the factors that drives flooding over an area. Supervised image classification was adopted to carry out the classification. The process involves composing bands 5 (Near Infrared), 4 (Red) and 3 (Green) using ArcGIS composite bands tool, this tool concatenate multiple bands into one to create a coloured raster which can be displayed over RGB, this image is then clipped to the study area. Image clipping is the art of cutting an image to a shape extent. This tool takes in a raster file to be clipped as input raster, a shapefile as extent and output raster specifying the location and type of the image to be produced as raster. In order to carryout supervised image classification, training sites were selected to identify areas with pre-defined LULC classes. These areas are water body, bareland, built up and vegetation.\u003c/p\u003e \u003cp\u003eDigital Elevation Model (DEM) was another dataset obtained from the same USGS\u0026rsquo; Earth explorer website as SRTM. This dataset was clipped to the study area to create the DEM of Damaturu Metropolis. Aspect and slope of the study area were also analysed. Weighted overlay analysis was used in GIS to compute flood susceptibility map of the study area. The weights are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The location of certain areas of interests were obtained using GPS and are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eClasses of parameters and their weights\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e(Source: Khosravi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeights\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximity to River\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6\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=\"c2\"\u003e \u003cp\u003e2001\u0026ndash;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4001\u0026ndash;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5001\u0026ndash;6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6001\u0026ndash;8000+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\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=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\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=\"c2\"\u003e \u003cp\u003e200\u0026ndash;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u0026ndash;600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e600\u0026ndash;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\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=\"c2\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eGeographical location of areas within Damaturu Metropolis\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\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSabon Fegi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 58'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 44'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFederal Polytechnic Damaturu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 59'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 44'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYobe State Assembly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 59'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 44'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState Secretariat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 59'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 44'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaziri Ibrahim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 59'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 45'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFederal HIgh Court\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 59'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 45'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePompomari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 58'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 45'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePompomari West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 58'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 45'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuhari Housing Estate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 57'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 45'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuma'at Mosque\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 58'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 44'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment House\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 57'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 44'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 57'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 43'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbba Ibrahim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 56'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 43'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfghanistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 55'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 44'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBayan Tasha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 57'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 44'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNayi Nawa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 57'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 44'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShagari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 57'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 45'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAli Marami\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 56'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 45'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTijjani Zanna Zakariyya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 56'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 46'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinistry of Works\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 57'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 46'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYobe State University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 56'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 40'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDon Etebe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026deg; 59'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026deg; 43'\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cp\u003eThe LULC map of Damaturu Metropolis (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows a concentration of built up at the centre of the metropolis, water bodies showing the flow path through which water passes through the metropolis lies from northeast to southeast of the metropolis. Given that, the image was obtained during rainy season (September, 2023), a significant amount of vegetation is observed. The DEM, Aspect, Slope and susceptibility map are shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e to 7. The distribution of LULC over the study area is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Pie chart of the distribution is shown in Fig.\u0026nbsp;3. Flow direction and watershed are also shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e8\u003c/span\u003e and 9.\u003c/p\u003e \u003cp\u003e \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\u003eDistribution of LULC classes over Damaturu\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=\"char\" char=\".\" 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\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt Up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1837.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10%\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1216.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11461.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBareland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4382.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18898.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\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 \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eFrom the flood susceptibility map of Damaturu, Nayi nawa, Shagari, Pompomari, Federal High Court, Waziri Ibrahim are found to have fallen in highly susceptible areas, whereas Tijjani Zanna Zakariya, Ministry of Works, Malari, Sabon Pegi, Bayan Tasha, Don Etebe were found to be less susceptible and therefore suitable for human development. Thus, GIS and remote sensing have proven in this study to be a reliable system for mapping flood susceptibility model using weighted overlay analysis.\u003c/p\u003e"},{"header":"5 Recommendations","content":"\u003cp\u003eIn a world of consistent, continuous technological and human advancement, prevention and mitigation of devastating natural is possible through consistent monitoring of the environment and examining the drivers of those natural hazards. Flood susceptibility assessment should be integrated into all government infrastructural provisions to avoid development of houses on flood plains. Thus, ensuring the safety of people and their wellbeing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Statement: The authors declare no conflict of interest and have adhered to all applicable ethical guidelines. The data and methods used in the study are available upon request. The study was funded by the researchers. The authors thank the study participants and the local communities for their cooperation and support. The study results will be used to inform flood risk management strategies in Damaturu Central, Yobe State, Nigeria, with the aim of reducing the negative impacts of floods on communities and improving the overall resilience of the area.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1. 'Shitu Shehu Usman carried out the data collection, geospatial analysis and proof reading.2. Nuhu Yerima Ngurnoma write the manuscript for draft submission and presentation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas A. G. \u0026amp; Mahboobeh H. (2020). Application of Flood Hazard Potential Zoning by using AHP Algorithm. Civil Eng Res J 9(5): CERJ.MS.ID.555775 (2020).\u003c/li\u003e\n\u003cli\u003eAbdullahi M. \u0026amp; Sadiq A. Y. (2020). An Analysis of the Flood-Prone Areas in Birnin Kebbi, Using Remote Sensing and GIS. Journal of Geography, Environment and Earth Science International 24(6): 77-85. DOI: 10.9734/JGEESI/2020/v24i630237\u003c/li\u003e\n\u003cli\u003eBureau of Meteorology. (2017). Guidelines for flood hazard mapping and floodplain management in Australia.\u003c/li\u003e\n\u003cli\u003eDottori, F., Simoncelli, S., \u0026amp; Todini, E. (2013). Flood hazard assessment using historical data and GIS: A case study of the Po River, Italy. Natural Hazards and Earth System Sciences, 13, 1511-1522.\u003c/li\u003e\n\u003cli\u003eGoudie, A.S. (2013). The human impact on the natural environment: Past, present, and future. Wiley-Blackwell.\u003c/li\u003e\n\u003cli\u003eHegazy, M. (2018). The Nile: Its people, history, and culture. American University in Cairo Press.\u003c/li\u003e\n\u003cli\u003eKhosravi, K., Panahi, M., \u0026amp; Pourghasemi, H. R. (2020). Flood hazard assessment using remote sensing and machine learning: A review. Remote Sensing of Environment, 248, 111981.\u003c/li\u003e\n\u003cli\u003eLevee, B. (2012). River cities: A history. University of Chicago Press.\u003c/li\u003e\n\u003cli\u003eLillesand, T. M., Kiefer, R. W., \u0026amp; Chipman, J. W. (2018). Remote sensing for flood hazard assessment. Springer Nature.\u003c/li\u003e\n\u003cli\u003eMcNeill, J. R. (2000). Something new under the sun: An environmental history of the twentieth-century world. New York: W.W. Norton \u0026amp; Company.\u003c/li\u003e\n\u003cli\u003eNingi A., Talib A., Bint L., Paim H. j. \u0026amp; Gill S. (2015). Cultural Dynamics of Child Labour in Yobe State Nigeria. International Journal of Humanities and Social Science. 20. 71-79. 10.9790/0837-20567179. \u003c/li\u003e\n\u003cli\u003eOuma, Y. O., Tateishi, R., \u0026amp; Honda, Y. (2015). Flood hazard assessment using remote sensing and GIS: A review. Journal of Hydroinformatics, 17, 1198-1224.\u003c/li\u003e\n\u003cli\u003eUsman S. (2022). Flood disconnects road linking Damaturu, Dapchi, other communities in Yobe. Daily Post Nigeria. https://dailypost.ng/2022/09/12/flood-disconnects-road-linking-damaturu-dapchi-other-communities-in-yobe/\u003c/li\u003e\n\u003cli\u003eWasko, C., Nathan, R. J., \u0026amp; Seed, A. (2015). Flood hazard assessment using historical data: A case study of the Brisbane River, Australia. Journal of Hydrology, 524, 469-482.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Flood susceptibility mapping, Damaturu, Yobe State, Geographic Information Systems (GIS), Remote Sensing, Analytical Hierarchy Process (AHP), Weight of Evidence (WoE), flood risk assessment, disaster preparedness, flood management strategies, resilience","lastPublishedDoi":"10.21203/rs.3.rs-3909114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3909114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study focuses on flood susceptibility mapping in Damaturu, Yobe State, Nigeria, leveraging Geographic Information Systems (GIS) and remote sensing techniques. Damaturu is prone to recurring flood events, necessitating effective flood mitigation and risk assessment strategies. Through the integration of GIS and remote sensing data, this research develops a robust flood susceptibility model. The study incorporates various data sources, including digital elevation models, hydrological data, and land-use maps, to create a comprehensive spatial database. Remote sensing data obtained from satellite and aerial platforms facilitate land cover change detection, flood extent identification, and flood-related damage assessment. The flood susceptibility mapping process employs GIS-based techniques, such as Analytical Hierarchy Process (AHP) and Weight of Evidence (WoE), to analyze and integrate the datasets, ultimately generating flood susceptibility maps for the area. These maps offer essential insights into flood-prone regions, aiding in flood risk assessment, disaster preparedness, and the development of targeted flood management strategies. The research outcomes are invaluable for policymakers, urban planners, and emergency response teams, enabling informed decision-making and proactive flood mitigation measures. The integration of GIS and remote sensing technologies ensures a comprehensive and adaptable approach to combat the challenges posed by floods in Damaturu, enhancing the resilience of local communities to future flood events.\u003c/p\u003e","manuscriptTitle":"Geospatial Analysis of Flood Susceptible Areas in Damaturu Central, Yobe State, Nigeria.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 18:22:53","doi":"10.21203/rs.3.rs-3909114/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a64952d-fdff-476c-b59b-e4081cac7776","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-12T11:36:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 18:22:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3909114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3909114","identity":"rs-3909114","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}