Developing Optimal Firefight Station Using Geospatial Techniques: A Case Study of Hosanna Town

Developing Optimal Firefight Station Using Geospatial Techniques: A Case Study of Hosanna Town | 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 Developing Optimal Firefight Station Using Geospatial Techniques: A Case Study of Hosanna Town Aster Chalchisa, Yeshiemebet Sirawdink Sirawdink2, Helen Tsegaye, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5571915/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 Proper management of fire risks is essential for protecting communities, and determining the best sites for firefighting facilities is a fundamental part of this strategy. Geospatial technology provides strong decision support for pinpointing these ideal locations. However, the use of geospatial technology for planning fire locations has not been widely investigated in Ethiopia. This research aims to create an effective strategy for fire location selection in Hosanna Town by utilizing advanced geospatial methods. This study used ALOS DEM, demographic data, and comprehensive field assessments to determine the key elements that impact the selection of fire locations. The Analytical Hierarchy Process (AHP) was used to give weights to these elements, which consist of slope, drainage density, population density, distance to service areas, and distance to existing fire stations. By applying the AHP method, thematic maps for each of these factors were created, which were subsequently merged through a weight overlay technique to identify the most suitable sites for new fire stations. At present, the current fire facilities cater solely to a single kebele, revealing a noteworthy gap in coverage. To remedy this deficiency, the study advises the creation of nine additional fire stations in line with literature and international benchmarks. Ultimately, the use of geospatial tools in planning fire locations has shown to be very effective. The research emphasizes the importance of integrating these technologies for enhanced site selection and infrastructure development, advocating for their incorporation into future fire risk management plans Analytical hierarchy process (AHP) Fire Station Geospatial technology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Public safety is seriously threatened by urban fires in low- and middle-income nations. The incidence and intensity of urban fires have been rising, particularly in developing nations like Africa (Aleisa 2018, Han et al., 2021,). Urban planning requires efficient firefighting systems since fires frequently result in a large loss of lives, property, and resources. The location of fire stations in quickly expanding cities like Hosanna continues to be a major problem in spite of these obstacles. Ethiopia's current fire stations are frequently in awkward locations, which causes poor service coverage and slow response times. Ethiopia's existing fire station design procedure mostly uses antiquated techniques that do not take advantage of contemporary geospatial technologies ((fekerete arega and muse belaw , 2014). The absence of geospatial-based fire station site selection is one of the biggest gaps in fire risk management. Due to a lack of knowledge about these tools and a lack of qualified personnel in the nation, Ethiopia has not made extensive use of GIS and remote sensing technologies for this purpose (fekerete arega and muse belaw 2014). There is a need for more accurate and effective methods because traditional approaches to fire station design, including abstract mathematical models, are often laborious and imprecise (Lai et al., 2011). On the other hand, the combination of GIS and Analytical Hierarchy Process (AHP) has demonstrated potential in determining the best locations for fire stations since it enables the incorporation of several elements, including accessibility, Closeness to high-risk locations and population density (Ayele, Wondim, & Abebe, 2018).In resource-constrained environments, like Ethiopia, where data collection and computational resources may be limited, GIS paired with AHP provides a simpler and more efficient solution for site selection than other approaches like machine learning or network analysis. With around four fatalities and about 2 million Ethiopian Birr lost each year due to fire occurrences, Hosanna, one of Ethiopia's fastest-growing cities, is particularly vulnerable to fire-related hazards (Tzeng et al. 1999).The main causes of these losses are the inaccessibility of the current fire services and the distance to the fire station (Hossana Town Municipality, personal communication). This study aims to fill the research gap through Geospatial technology and AHP to propose an optimal fire station location for Hosanna. By leveraging geospatial tools, this research seeks to improve fire risk management, response times, and overall public safety, offering a comprehensive solution to fire station placement in Ethiopian urban settings. Significance of Study Urban fire is one of the most important problems for developing and developed countries (Turan Erden & Coskun, Z. 2010). With Geographic information systems (GIS), it is possible to achieve efficient results in applications based on spatial information; GIS can analyse intensive data volumes and is highly effective in responding to spatial queries that can be used to analyse data concerning urban fires (Badri et al. 1998). The finding from this study is expected to have the following significance to city/ rural Policy makers, designers, and stakeholders by: Helping designers to know geospatial and environmentally friendly tools for optimal site selection for fire location. Allowing urban/rural planners to know what social and environmental factors should be included for optimal site selection for fire location. Assisting fire risk manager experts provide a scientific basis for selecting the optimal location for fire using geospatial tools to reduce the risk of fire emergencies. 4. Providing policy makers scientific based information to develop the strategic policy for optimum site selection for fire location. 5. Allowing study area beneficiaries to reduce the fire risk by providing them with available firefight service accordingly within less than 5 minutes Study Area Description Hosaena (also spelled Hosaina or Hosa'ina, also called Hosanna (which has namesakes); an older name is Wachamo) is a town and separate woreda in southern Ethiopia, and the administrative centre of the Hadiya Zone. Located in the Southern Nations, Nationalities, and People's Region (SNNPR), Hosaena has a latitude and longitude of 7°33′N 37°51′ECoordinates: 7°33′N 37°51′E with an elevation of 2177 meters above sea level. Methods Data Collection Methods The initial step in determining the best location for a firefighting station involves gathering trustworthy information on geology, land use, slope, response time to accidents, and the road network within the study area. Primary and secondary data were utilised to meet the study’s objectives. The primary data source in this research used GPS, while the Secondary data, such as satellite imagery, digital elevation models (DEM), study area boundaries, structural plans, geological maps, topographic maps, village layouts, road networks, population density, and accident response times, and were collected from appropriate sources. Data Sources and Materials This research includes a compilation of both spatial and non-spatial data obtained from actual fieldwork as well as secondary data from various resources. Table 1 outlines the types of data employed in the study, their formats, and the sources from which they will be obtained. Table 1: Data source S. No Data Source Data Type Cell Size/ Scale Purpose 1 DEM https://asf.alaska.edu TIFF 12.5m Drainage density and slope of study was extracted from it 2 Structural plan municipality Shape file _ To identify the main service area 4 town boundary BOFED,2020 Shapefile - For study area boundary delineation. 5 Population density Hossana ,CSA Excel data - Population density map Data Analysis Techniques Planning the location of a facility requires consideration of a number of important factors, including available space, measurement criteria, demand points, and possible service centres(KABITE et al. 2012). In order to maximise service coverage or minimise response time and expenses, the best places for service centres must be identified, and their assignment to demand points must be decided (Davoodi, 2019). In the case of fire station location planning, several factors must be considered, including population density, proximity to critical infrastructure, topography, and existing resources. This process involves modeling, formulating, and solving location-based problems to determine the best locations for emergency services. Fire Station Location Planning For effective service delivery, emergency employees must respond quickly after an incident is reported. This requires ensuring that fire stations are strategically located to minimize response times and cover high-risk areas effectively(Twigg 2017).The fire station location planning process in this study follows these steps: 1. Determining the criteria for selecting the best fire station locations. 2. Creating a database model for each criterion. 3. Converting the datasets into a suitable format (raster format). 4. Assigning weights to each criterion using the Analytical Hierarchy Process (AHP). 5. Identifying the most optimal site for a fire station. Determining the criteria for the optimal fire station location This study identifies and defines five critical criteria based on available data and existing literature that are relevant to the study area. These criteria include: Distance to Service Area: The fire station location should minimize travel time to key service areas to ensure quick response during emergencies. Population Density: Areas with high population density should be prioritized to ensure that emergency services reach the greatest number of people within the shortest time. Slope: The station must be located on level ground to ensure efficient access to high-risk areas and avoid complications due to steep terrain. Drainage: Good drainage conditions are crucial for emergency access and water availability for firefighting. Existing Fire Stations: The location of existing fire stations is an important factor to ensure that new stations are well-distributed and can cover underserved areas. Distance to service area Fire stations should be situated to efficiently serve the neighbourhood and guarantee accessibility in a timely manner. To determine the ideal locations for the fire station, the distance between possible sites and important service regions was computed and examined using GIS technologies. Population density An Excel document containing Hosanna’s population data was used to construct population density maps that prioritized locations with a higher risk of fire. With a goal of limiting emergency vehicles' travel time to no more than five minutes, this map assisted in identifying locations for the fire station. Slope Slope ArcGIS 10.8 was used to analyses slope data that was collected from the Alaska Satellite Facility (ASF). In order to prevent fire stations from being situated in locations that would make it difficult to reach them in an emergency, the land was divided into four distinct slope groups according to how steep they were. Drainage Drainage The ALOS DEM was used to analyses drainage conditions, which are essential for battling fires. Drainage density was then assessed using GIS hydrological techniques. Drainage density influences the choice of fire station locations by providing insight into flood risk and water availability for combating fires. Existing fire station In order to determine underserved areas, the location of existing fire stations was taken into consideration. To make sure that new fire stations are positioned strategically for the best coverage, GIS data on the locations of these stations was integrated. Analytical hierarchy process A popular multi-criteria decision-making technique that assists in selecting the best option from a range of options based on a number of criteria is the Analytical Hierarchy Process (AHP) (Kalamaras et al., 2000). Using Saaty's scale and pairwise comparisons, AHP was used in this work to assign weights to the selection criterion (Table 2). To rank possible locations according to their suitability for the location of fire stations, global weights were computed after local weights for each criterion were determined.As a multi-criteria decision-making (MCDM) method, AHP was originally developed by Saaty (Saaty, 1990, 1977, 1986) and involves pairwise comparisons to establish rankings. Weight Determination Using AHP In order to determine the relative relevance of each criterion using Saaty's scale, the first step in implementing AHP is to compare the criteria pairwise (Table 2). Following the completion of the pairwise comparisons, the local weights were determined using the results. To ascertain the overall significance of each alternate location, these weights were subsequently normalized and displayed in a final matrix. Rate of Consistency and Thresholds the consistency ratio (CR) was computed to assess the pairwise comparisons' consistency. Comparisons are deemed consistent if CR is less than 0.1 (Saaty, 1990). Inconsistencies in the judgements were resolved by rewriting the paired comparisons if CR was greater than 0.1. This stage guarantees the validity of the decision-making procedure and the final weights. Analysis of Sensitivity sensitivity study was conducted to evaluate the potential effects of changes in the allocated weights on the final site selection, given the subjectivity of AHP in assigning weights. This research guarantees that the selected fire station location is stable across various weight distributions and helps determine how sensitive the results are to shifts in expert opinions. Proposing Suitable Sites for Fire Station Location A final suitability map showing the best places for fire stations was created by integrating the data using ArcGIS after the AHP approach was used and the criteria were given weights. Since Ethiopia lacks a national standard for the placement of fire stations, the analysis took into account the weights given to each criterion to make sure the sites chosen satisfy the unique requirements of the research region as well as pertinent international norms. Table 2: Saaty’s scale RANK DESCRIPTION 1 Equally importance 3 Moderate importance 5 Strong importance 7 significant importance 9 Extremely importance Table 3: Consistency of random matrices Matrix order 1 2 3 4 5 6 RI 0 0 0.52 0.89 1.11 1.25 Results and Discussion With the completion of the data analysis, it is imperative to examine and carefully discuss the methods used for data analysis, as well as the result achieved. The processed data was displayed using tables, maps, and graphs to illustrate the outcomes. Initially, all criteria maps were created following the methodology outlined. Preparation Criteria Map for Each Factor Reclassified Slope layer As stated earlier, the slope of the study region was obtained from the PLASAR ALOS DEM, which features a resolution of 12.5 meters, using ARCGIS 10.8. The slope degree indicates how elevation varies across the landscape, where lower slope values signify flat areas, and higher values denote steeper terrain. In this research, the slope angle was categorized following the ERA (2013) classification into four groups: gentle (flat) slopes (0° - 2°), rolling slopes (2° - 14°), mountainous slopes (14° - 37°), and escarpments (>37°). The analysis indicated that gentle slopes, with angles under 2°, comprise 31.66% (11.65 square kilometres) of the overall area. Moderately steep or rolling slopes, ranging from 2° to 14°, encompass 36.213% (13.49 square kilometres) of the region, while mountainous slopes represent 24% (8.94 square kilometres) of the total area. Figure 1 shows the reclassified slope layers. To establish new fire stations, regions with flat or rolling slopes are considered appropriate due to their relatively gentle terrain, which aids in easier construction and ensures a rapid emergency response. In contrast, mountainous and escarpment zones, with steeper slopes are deemed less favourable for new fire station sites. Given the challenging terrain, these locations would incur significantly higher construction costs and pose obstacles to quick emergency response. Table 4 and Figure 3 display the reclassified slope layer. Fire spread is influenced by slope; steeper slopes cause fire to spread more quickly because of convection and fuel preheating (Rothermel, 1972). According to studies, fire spreads almost twice as quickly on slopes higher than 30° as it does on flat ground (Alexander, 1982). By Scott & Reinhardt's (2001) findings, steeper slopes were given higher weights because of the greater risk of fire propagation. Slope values were grouped according to fire behavior models, with slopes higher than 20° being categorized as high risk (Finney, 1998). Table 4: Reclassified Slope layer S/N Slope degree suitability Area Percent (%) 1 0-2 Highly suitable 7.38 19.9 2 2-14 suitable 13.72 36.99 3 14-37 Moderately suitable 11.99 32.29 4 >37 unsuitable 4.008 10.8 Reclassified Existing Fire Location Buffer analysis was performed around the current fire station locations, with buffers at one square kilometre, two square kilometres, three square kilometres, and four square kilometres utilized to evaluate the effectiveness of fire station coverage and the necessity for more stations. The findings suggest that the one square kilometre buffer, encompassing 7.38 square kilometres or 19.9% of the overall area, is inadequate and does not warrant the establishment of further fire stations, as the present coverage is satisfactory. The two square kilometre buffer, covering 13.72 square kilometres and representing 36.99% of the total area, offers slightly improved coverage but still falls short of fully addressing the need for additional fire stations. Conversely, the examination of the three square kilometres and four square kilometre buffers, which encompass 11.979 square kilometres (32.29% of the total area) and 4.008 square kilometres (10.8% of the total area) respectively, highlights regions that are presently unreachable by existing fire stations. These distances underscore significant coverage gaps, signalling a clear requirement for more fire stations to be located in these areas. The three square kilometre and four square kilometre buffers have been recognized as vital regions where new fire stations would improve emergency response. Figure 4 depicts the reclassified existing fire station locations. The proximity of fire stations significantly impacts emergency response time and fire containment efficiency. NFPA (National Fire Protection Association) guidelines recommend that urban fire stations be within 1 to 3 km of high-risk areas for optimal response (NFPA 1710, 2020). Weight Justification: Areas farther from fire stations were given higher risk weights, consistent with urban fire safety standards (González-Olabarria et al., 2012). Classification Justification: Buffer zones were defined based on response time efficiency, where distances beyond 3km were classified as high-risk (Hassan et al., 2021). Reclassified population densities Map Using ArcGIS 10.8, the population density map was classified into four groups, with Class 1 being the highest population density and Class 4 the lowest. Class 1 encompasses 52.26% of the entire 19.38 km² area, according to the data, indicating areas with the highest population density. The necessity for a greater concentration of fire stations in densely populated kebeles to enable efficient emergency response is supported by fire responders' oral reports, which indicate a higher incidence of fire occurrences in these locations. In contrast, regions with moderate to low population densities are represented by Class 3 (3.95 km2) and Class 4 (3.98 km2). The need for fire stations is anticipated to be lower in these areas because there have been less documented fire incidents. Furthermore, 9.78 km² of the entire area is categorised as sparsely populated, indicating that fewer fire stations may be required because of the decreased risk of fire outbreaks. While these findings indicate a correlation between population density and fire risk, the lack of documented historical fire incident data limits a precise validation of this relationship. Future studies should incorporate quantitative fire incident records to strengthen the analysis and compare alternative fire station placement models. This analysis emphasises the importance of strategically placing fire stations in areas with higher population density while considering additional factors such as accessibility and historical fire trends to enhance emergency response efficiency. Figure 5b shows the reclassified population density map. Because of increased ignition sources and infrastructural vulnerabilities, densely inhabited areas are more likely to have fires (Syphard et al., 2007). Research has shown that the frequency of fires and urban density are strongly correlated (Chuvieco et al., 2010).Following fire risk models utilised in urban hazard assessment, high-density areas were given higher weights (Cardille et al., 2001).Fire danger studies were used to determine population density limits, with locations with more than 5000 persons per km² being categorized as extremely high risk (Modugno et al., 2016). Reclassified Service Area Map The ideal sites for new fire stations were identified by examining how close they are to crucial service areas in the study region, such as police stations, hospitals, schools, marketplaces, and residential neighbourhoods. Using ArcGIS, these service areas were combined and then buffered at intervals from 500 meters to 3 kilometres, following guidelines found in existing literature. The buffering analysis was divided into four categories to evaluate the suitability of fire station placements. Class 1, which refers to a 500-metre buffer, is viewed as the most appropriate, as it reflects a close distance to vital services, allowing for rapid emergency response. Class 2, characterized by a 1-kilometre buffer, and is also considered suitable but not as optimal as Class 1. Class 3, associated with a 2-kilometre buffer, and is regarded as less suitable because the greater distance from service areas might result in longer response times. Lastly, Class 4, indicating a 3-kilometre buffer, is seen as unsuitable for the location of fire stations due to the considerable distance from key services, which could impede efficient emergency response. Consequently, the analysis advocates for prioritizing fire station sites within shorter buffer distances to enhance effectiveness and coverage. Figure 6 illustrates the reclassified service area map. The fire service area was determined based on fire station coverage and accessibility. Studies on fire station accessibility emphasize that response time is a critical factor in reducing fire damage (Kule et al., 2020). Higher weights were assigned to underserved regions, following spatial analysis techniques used in fire station optimization (Gibbons et al., 2018). Service areas were categorized into low (within 3 km), moderate (3–5 km), and high-risk zones (beyond 5 km), aligning with NFPA and urban planning standards. Reclassified Drainage Densities Map In determining the best sites for new fire stations, drainage densities of the region were assessed using the ALOS DEM data analysed through spatial tools in ArcGIS. This process was initiated by developing a stream order map, followed by the construction of a drainage density map utilising line density execution methods. The resulting drainage densities were divided into four separate categories, each reflecting different levels of drainage density within the study region, as shown in Figure 7. Class 1 encompasses regions with the lowest drainage densities, covering 15.18 square kilometres, which represents 40.9% of the overall study area. These locales, marked by a scarcity of drainage lines, are regarded as highly favourable for the establishment of new fire stations. The limited presence of drainage lines in these zones indicates a diminished risk of flooding, an essential factor for maintaining the accessibility and operational effectiveness of fire stations. Class 2, spanning 10.14 square kilometres or 27.34% of the study area, also displays relatively low drainage densities. While their drainage networks are somewhat more extensive than those of Class 1, these regions remain appropriate for fire station siting due to their manageable risk levels and accessibility. They strike a commendable balance between coverage requirements and risk management, making them a feasible secondary choice. On the other hand, Classes 3 and 4 show elevated drainage densities, occupying 8.05 square kilometres (21.67%) and 3.72 square kilometres (10.049%) respectively. These areas are less ideal for fire station placement because of the increased complexity and density of drainage systems, which could result in heightened flooding risks and challenges in accessibility, especially during emergencies. The elevated drainage density in these classes indicates a greater susceptibility to water accumulation and other hydrological complications, which could hinder the efficiency of fire station operations. In summary, the analysis distinctly shows that regions with lower drainage densities, particularly those belonging to Class 1, are the most appropriate for new fire station placements. Class 2 regions are usable as well, though with slightly higher risk considerations. Conversely, areas categorized in Classes 3 and 4 should be steered clear of due to the potential complications arising from their elevated drainage densities. This strategic method guarantees that new fire stations are situated in locations that enhance accessibility while minimizing environmental hazards, thus improving the overall effectiveness of emergency response services. The impact of water bodies and drainage systems on fire occurrence has been extensively documented in wildfire management studies (Tedim et al., 2016). Areas within 200m of drainage channels were classified as low-risk zones, in accordance with recommendations from fire risk zoning studies (Modugno et al., 2016). Areas near drainage were given lower weights due to their role in fire suppression, which is consistent with findings by Keeley et al. (2011). Drainage networks can serve as natural firebreaks, limiting spread and reducing fire intensity (Pyne et al., 1996). Analytical hierarchy process The Analytic Hierarchy Process (AHP) Consistency Ratio (CR) in this study was 0.008, which is well below the permissible threshold of 0.1 and shows that the pairwise comparisons done during the analysis are very consistent. When the CR value is less than 0.1, it indicates that the decision-makers' decisions were logically sound and free of major inconsistencies. This validates the validity and reliability of the AHP model and guarantees that the criteria prioritization and decision-making process are reliable and consistent (Hillier et al., n.d.,2021). The impact of water bodies and drainage systems on fire occurrence has been extensively documented in wildfire management studies (Tedim et al., 2016). Areas within 200m of drainage channels were classified as low-risk zones, according to recommendations from fire risk zoning studies (Modugno et al., 2016). Weight Justification: Areas near drainage were given lower weights due to their role in fire suppression, which is consistent with findings by Keeley et al. (2011). Drainage networks can serve as natural firebreaks, limiting the spread and reducing fire intensity (Pyne et al., 1996). Proposed a suitable site for fire location A suitability map was methodically prepared through the AHP (Analytical Hierarchy Process) extension tool, integrating five weighted parameters to measure the need for new fire stations within the area. The weights allocated to each parameter were grounded on different international standards, a systematic literature review, and the nature of the study area. Table 5: Weight assigned to criterion ID CRITERION WEIGHT (%) RANK 1 Population Density 25 1 2 Service Area Map 20 2 3 Existing Fire Stations 20 2 4 Drainage Density 20 2 5 Slope 15 3 The parameters and their respective weights were as shown in Table 4 above. The analysis outcomes deliver critical insights into the region's fire station needs. It was resolute that 3.21 square kilometres, creating approximately 9.16% of the total area, are in serious need of a new fire station. Additionally, areas covering 13.37 square kilometres and 13.91 square kilometres, which account for 37.9% and 38.8% of the area, respectively, have been identified as requiring fire stations. These regions have been classified into three categories: Class 1, indicating an immediate need; Class 2, indicating a moderate need; and Class 3, indicating a lower priority. Furthermore, the analysis established that 5.29 square kilometres of the total area, representing 14.74%, fall under Class 4. These areas are already adequately covered by existing fire stations, indicating no necessity for additional stations in these zones. This inclusive assessment highlights the importance of deliberate planning in augmenting fire safety infrastructure across the region. Figure 8 shows a suitability map for the fire station Proposed Number of New Fire Locations A suitability map was developed to identify areas that are in need of fire station service based on designated criteria. This suitability map was converted into vector format, and the highest priority areas (Class 1), demonstrating regions with the highest need for fire stations, were extracted. A grid index with a 3 km distance was created using the Grid Index Features, a Data Management tool in ArcGIS. This grid was used for the Class 1 vector data, and a fishnet analysis was then conducted to generate point data. These points represent potential locations for fire stations within the identified high-need areas. The created grid index and fishnet are shown below in Figure 9. This process results in a set of specific locations where fire stations should be established to serve the community effectively based on the selected criteria. The Final proposed fire location After creating point data using the fishnet technique, the study area’s kebeles in Hossana town were overlaid with the grid index and fishnet results. This overlay analysis allowed the identification of the required number of fire station locations within each kebele. The investigation results, which detail the number of essential fire station locations for each kebele in Hossana town, are illustrated in the map below. Based on the analysis, the considered positioning of fire stations was identified as a critical factor, together with population density and proximity to service areas such as hospitals, residential, and commercial zones. This supports findings from other studies, including Wright et al. (2020), which emphasised the significance of locating fire stations near high-risk and densely populated areas to increase service effectiveness and ensure immediate response times. The proposed fire station locations for each kebele are as follows: Heto : Previously had no fire station; now, one fire station is proposed. Lechamba : Previously had no fire station; now, one fire station is proposed. Arada : Formerly had one fire station serving the entire town; now, two fire stations are proposed. Seche Duna : Previously had no fire station; now, three fire stations are proposed based on standard requirements. Naramao : Previously had no fire station; now, one fire station is proposed. Bobocho : Previously had no fire station; now, one fire station is proposed. Table 6, summarises the number of fire stations before and after the proposal for each kebele in Hossana town. Table 6: Proposed fire location for each Kebele in Hossana Town kebele Fire Stations Before Proposed Fire Stations Heto 0 1 Lechamba 0 1 Arada 1 2 Seche Duna 0 3 Naramao 0 1 Bobocho 0 1 Discussion By using multi-criteria decision analysis and GIS technologies, this study sought to determine the best places for fire stations in an urban setting. By integrating engineering, environmental, and social factors into a unified spatial framework, it improves upon existing urban planning techniques. Optimal fire location According to the analysis, the most important determinant of appropriate fire station placements is population density. This is in line with research by Johnson & Lee (2021) and Smith et al. (2019), who highlighted that locations with a high population density create more emergency demand and so need priority service coverage. The study adds to the generalizability of this criterion in fire station layout by confirming this link in a new geographic setting. Furthermore, the study shows that the second most important factor is closeness to vital service sectors, such as residential neighborhoods, schools, hospitals, and existing fire infrastructure. This bolsters Wright's (2018) claim that improving service responsiveness and reducing response time requires proximity to critical and vulnerable infrastructure. The necessity of integrated urban infrastructure design is further supported by this conclusion, which also demonstrates that fire station layout cannot be divorced from larger urban service distribution patterns. Integration of Environmental and Engineering Parameters. This analysis includes environmental factors, including slope and drainage density, in contrast to earlier models that mostly focused on population and accessibility. Although frequently disregarded, these elements have a big impact on how well emergency services operate. For instance, in an emergency, vehicles may find it difficult to access regions with poor drainage or steep slopes. By taking these limitations into account, the study guarantees risk-aware infrastructure development and long-term functional viability in addition to suggesting ideal sites. Scientific Contribution and Broader Implications Methodologically, a flexible and reproducible model for multi-criteria spatial decision-making is provided by the combination of AHP and SMCDA. The structure of the model improves transparency and permits scenario-based testing, even though the application of AHP adds subjectivity to the weighting process. This method adds to the body of literature on urban planning by showing how spatial analytics can be used to organize and assess expert knowledge. Crucially, the approach takes into consideration fairness (making sure under-represented regions are covered) and efficiency (reducing response time), a balance that is frequently absent from traditional fire station layouts. Data-driven investment in emergency services is supported by the geographical findings of this study, which directly feeds policy by indicating priority areas for new infrastructure. Conclusion Identification of optimal location This study shows that combining AHP with SMCDA offers a transparent and methodical approach for determining the best places for fire stations. The approach facilitates well-informed decision-making that goes beyond instinctive or politically motivated siting by taking into consideration variables like population density, fire risk, accessibility, and environmental limits. The findings support more equitable emergency response planning by showing that the suggested locations greatly improve coverage in high-need areas and decrease service inequities. Assessment of Current Fire Stations There are significant discrepancies between the sites of current fire stations and high-risk and densely populated areas, according to a spatial analysis of existing stations. This research identifies areas with inadequate service as well as those where station location is not strategically in line with modern urban reality. Some stations are misaligned and could need to be moved, while others need to be upgraded. These findings imply that maintaining operational success requires regular reevaluations of station locations based on spatial criteria. Service Area Analysis and Coverage Improvements The proposed station network offers more equal and comprehensive geographic coverage, especially in formerly underserved neighborhoods, according to the service area study. The optimized configuration increases access for at-risk populations and reduces projected response times when compared to the current structure. In addition to being operationally important, these results also advance more general objectives of public safety and urban resilience. Environmental and Cost Considerations the methodology encourages sustainable design by incorporating environmental and financial assessment into the site selection procedure. Locations that avoid environmentally sensitive areas, use pre-existing infrastructure, and save construction costs were prioritized. This method offers a reproducible paradigm for sustainable emergency facility planning in urban settings while reducing long-term operational and environmental constraints. Risk Reduction and Accessibility High accessibility is emphasized by the optimized station location method, especially when it comes to areas that are prone to fire and road networks. The system's capacity to react swiftly to crises is improved by this geographical prioritization, especially in places that are susceptible. As a result, the approach encourages fire response infrastructure to transition from reactive to proactive planning. Limitations and Future Considerations Although the model offers a well-organized and useful framework, there are still certain restrictions. Although useful for multi-criteria decision-making, the AHP method includes subjectivity through expert judgement in weight assignment. The temporal accuracy of the model may also be impacted by the lack of real-time fire incident data. Subsequent investigations ought to examine the incorporation of dynamic data sources, such as past fire logs and current sensor data, and assess the framework's applicability in various urban settings. Recommendation Integration of Geographic Information Systems (GIS) in Firefighting Site Selection: When choosing a firefighting site, urban planners, legislators, and designers ought to consider Geographic Information Systems (GIS). With the use of extensive geographical analysis made possible by GIS, the best locations are found by taking into account social and environmental aspects. Critical Elements to Take Into Account When Choosing a Firefighting Location: When choosing a firefighting location, planners should take into account a number of elements, such as population density, proximity to high-risk fire zones, accessibility, infrastructure, and the environmental impact. To guarantee optimum effectiveness and coverage, these elements ought to direct the best possible placement of firefighting services. Evaluation of Current Firefighting Sites: It is critical to evaluate fire stations' existing sites regularly to ensure they still serve the community's needs and the appropriateness of their current locations. Developing New Firefighting Locations with the Help of Geospatial Analysis. Advanced geospatial techniques should be used to propose new places when it is determined that the current locations are insufficient. By using this strategy, it will be possible to ensure that the new locations are positioned to offer improved coverage in areas that are prone to fires and quicker reaction times. Development of Strategic Policies Based on Scientific Research: When developing regulations meant to lower the risk of fire, policymakers should take this study's conclusions into consideration. The strategic location of firefighting stations should be the main focus of these strategies, with data-driven station placement that satisfies both urban and rural safety regulations. Declarations Funding Declaration: This research received no specific grant from any funding agency, commercial, or not-for-profit sectors. Ethics, Consent to Participate, and Consent to Publish Declarations Not applicable. Data Availability Declaration the data that support the findings of this study are available from the corresponding author upon reasonable request. Competing Interest Declaration the authors declare that they have no competing interests. References Aleisa, Esra. 2018. “The Fire Station Location Problem: A Literature Survey.” International Journal of Emergency Management 14(3): 291. Amlashi, B Azmoudeh. 2019. “The Role of Optimal Site Selection of Fire Stations in Urban Safety.” 4(3): 223–38. Ayele, Leykun Getaneh, Yirga Kebede Wondim, and Abiyu Demessie Abebe. 2018. “GIS Based Suitable Site Selection and Road-Map Preparation for Equitable Distribution of Secondary Schools of Amhara Region ,.” Journal of Environment and Earth Science 8(1): 100–113. Badri, Masood A., Amr K. Mortagy, and Colonel Ali Alsayed. 1998. “A Multi-Objective Model for Locating Fire Stations.” European Journal of Operational Research 110(2): 243–60. Davoodi, Mojtaba. 2019. “A GIS Based Fire Station Site Selection Using Network Analysis and Set Covering Location Problem (Case Study: Tehran, Iran).” (December 2018): 433–36. Erden, T, and M Z Cos. 2010. “Multi-Criteria Site Selection for Fire Services : The Interaction with Analytic Hierarchy Process and Geographic Information Systems.” (1980): 2127–34. Erden, Turan, and Mehmet. Coskun, Z. 2010. “The Role of Geospatıal Tools in Dısaster Management Life Cycle.” FIG Congress Facing the Challenges- Building the Capacity (January): 11–16. fekerete arega and muse belaw. 2014. “GIS and Remote Sensing in Highway Route Selection : A Case Study in Ethiopia , Selection of the Addis Ababa - Nazret Expressway Alignment.” (August). Gelan, Eshetu. 2021. “GIS-Based Multi‐criteria Analysis for Sustainable Urban Green Spaces Planning in Emerging Towns of Ethiopia: The Case of Sululta Town.” Environmental Systems Research 10(1). Han, Bing, Mingxing Hu, Jiemin Zheng, and Tan Tang. 2021. “Site Selection of Fire Stations in Large Cities Based on Actual Spatiotemporal Demands : A Case Study of Nanjing City.” KABITE, GIZACHEW, MEKURIA ARAGAW, and HAMEED SULAIMAN. 2012. “GIS-Based Solid Waste Landfill Site Selection In.” International Journal of Ecology and Environmental Sciences 38 (2-3): 59-72, 2012 © NATIONAL INSTITUTE OF ECOLOGY, NEW DELHI 38(December): 59–72. Kalamaras, G S, L Brino, G Carrieri, and C Pline. 2000. “Application of Multicriteria Analysis to Select The.” Lai, W E I et al. 2011. “Study and Implementation of Fire Sites Planning Based on GIS and AHP.” 11: 486–95. http://dx.doi.org/10.1016/j.proeng.2011.04.687. Ş, A, İ Önden, T Gökgöz, and C Şen. “A GIS APPROACH TO FIRE STATION LOCATION SELECTION.” Twigg, John et al. 2017. “Improved Methods for Fire Risk Assessment in Low-Income and Informal Settlements.” International Journal of Environmental Research and Public Health 14(2). Tzeng, Gwo Hshiung, and Yuh Wen Chen. 1999. “The Optimal Location of Airport Fire Stations: A Fuzzy Multi-Objective Programming and Revised Genetic Algorithm Approach.” Transportation Planning and Technology 23(1): 37–55. Wang, Wenxuan. 2019. “Ingénierie Des Systèmes d ’ Information Site Selection of Fire Stations in Cities Based on Geographic Information System and Fuzzy Analytic Hierarchy Process.” 24(6): 619–26. Wu, Chia Hao, and Liang Chien Chen. 2012. “3D Spatial Information for Fire-Fighting Search and Rescue Route Analysis within Buildings.” Fire Safety Journal 48: 21–29. http://dx.doi.org/10.1016/j.firesaf.2011.12.006. Additional Declarations No competing interests reported. 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19:01:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2647368,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5571915/v1/2f6dd096-1246-4259-b730-3d1abbbba27d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDeveloping Optimal Firefight Station Using Geospatial Techniques: A Case Study of Hosanna Town\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePublic safety is seriously threatened by urban fires in low- and middle-income nations. The incidence and intensity of urban fires have been rising, particularly in developing nations like Africa\u0026nbsp;(Aleisa 2018, \u0026nbsp;Han et al., 2021,).\u003c/p\u003e\n\u003cp\u003eUrban planning requires efficient firefighting systems since fires frequently result in a large loss of lives, property, and resources. The location of fire stations in quickly expanding cities like Hosanna continues to be a major problem in spite of these obstacles. Ethiopia\u0026apos;s current fire stations are frequently in awkward locations, which causes poor service coverage and slow response times. Ethiopia\u0026apos;s existing fire station design procedure mostly uses antiquated techniques that do not take advantage of contemporary geospatial technologies ((fekerete arega and muse belaw\u0026nbsp;, 2014).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The absence of geospatial-based fire station site selection is one of the biggest gaps in fire risk management. Due to a lack of knowledge about these tools and a lack of qualified personnel in the nation, Ethiopia has not made extensive use of GIS and remote sensing technologies for this purpose (fekerete arega and muse belaw 2014). There is a need for more accurate and effective methods because traditional approaches to fire station design, including abstract mathematical models, are often laborious and imprecise (Lai et al., 2011). On the other hand, the combination of GIS and Analytical Hierarchy Process (AHP) has demonstrated potential in determining the best locations for fire stations since it enables the incorporation of several elements, including accessibility,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Closeness to high-risk locations and population density (Ayele, Wondim, \u0026amp; Abebe, 2018).In resource-constrained environments, like Ethiopia, where data collection and computational resources may be limited, GIS paired with AHP provides a simpler and more efficient solution for site selection than other approaches like machine learning or network analysis. With around four fatalities and about 2 million Ethiopian Birr lost each year due to fire occurrences, Hosanna, one of Ethiopia\u0026apos;s fastest-growing cities, is particularly vulnerable to fire-related hazards (Tzeng et al. 1999).The main causes of these losses are the inaccessibility of the current fire services and the distance to the fire station (Hossana Town Municipality, personal communication). This study aims to fill the research gap through Geospatial technology and AHP to propose an optimal fire station location for Hosanna. By leveraging geospatial tools, this research seeks to improve fire risk management, response times, and overall public safety, offering a comprehensive solution to fire station placement in Ethiopian urban settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance of Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUrban fire is one of the most important problems for developing and developed countries (Turan Erden \u0026amp; Coskun, Z. 2010). With Geographic information systems (GIS), it is possible to achieve efficient results in applications based on spatial information; GIS can analyse intensive data volumes and is highly effective in responding to spatial queries that can be used to analyse data concerning urban fires\u0026nbsp;(Badri et al. 1998). The finding from this study is expected to have the following significance to city/ rural Policy makers, designers, and stakeholders by:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u0026nbsp;Helping designers to know geospatial and environmentally friendly tools for optimal site selection for fire location.\u003c/li\u003e\n \u003cli\u003eAllowing urban/rural planners to know what social and environmental factors should be included for optimal site selection for fire location.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Assisting fire risk manager experts provide a scientific basis for selecting the optimal location for fire using geospatial tools to reduce the risk of fire emergencies.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e4. Providing policy makers scientific based information to develop the strategic policy for optimum site selection for fire location.\u003c/p\u003e\n\u003cp\u003e5. Allowing study area beneficiaries to reduce the fire risk by providing them with available firefight service accordingly within less than 5 minutes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Area Description\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHosaena (also spelled Hosaina or Hosa\u0026apos;ina, also called Hosanna (which has namesakes); an older name is Wachamo) is a town and separate woreda in southern Ethiopia, and the administrative centre of the Hadiya Zone. Located in the Southern Nations, Nationalities, and People\u0026apos;s Region (SNNPR), Hosaena has a latitude and longitude of 7\u0026deg;33\u0026prime;N 37\u0026deg;51\u0026prime;ECoordinates: 7\u0026deg;33\u0026prime;N 37\u0026deg;51\u0026prime;E with an elevation of 2177 meters above sea level.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData Collection Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial step in determining the best location for a firefighting station involves gathering trustworthy information on geology, land use, slope, response time to accidents, and the road network within the study area. Primary and secondary data were utilised to meet the study\u0026rsquo;s objectives. The primary data source in this research used GPS, while the Secondary data, such as satellite imagery, digital elevation models (DEM), study area boundaries, structural plans, geological maps, topographic maps, village layouts, road networks, population density, and accident response times, and were collected from appropriate sources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Sources and Materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research includes a compilation of both spatial and non-spatial data obtained from actual fieldwork as well as secondary data from various resources. Table 1 outlines the types of data employed in the study, their formats, and the sources from which they will be obtained.\u003c/p\u003e\n\u003cp\u003eTable 1: Data source\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"708\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.47458%;\"\u003e\n \u003cp\u003e\u003cem\u003eS. No\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7966%;\"\u003e\n \u003cp\u003e\u003cem\u003eData\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2429%;\"\u003e\n \u003cp\u003e\u003cem\u003eSource\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5706%;\"\u003e\n \u003cp\u003e\u003cem\u003eData Type\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e\u003cem\u003eCell Size/ Scale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5085%;\"\u003e\n \u003cp\u003e\u003cem\u003ePurpose\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.47458%;\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7966%;\"\u003e\n \u003cp\u003e\u003cem\u003eDEM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2429%;\"\u003e\n \u003cp\u003e\u003cem\u003ehttps://asf.alaska.edu\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5706%;\"\u003e\n \u003cp\u003e\u003cem\u003eTIFF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e12.5m\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5085%;\"\u003e\n \u003cp\u003e\u003cem\u003eDrainage density and slope of study was extracted from it\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.47458%;\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7966%;\"\u003e\n \u003cp\u003e\u003cem\u003eStructural plan\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2429%;\"\u003e\n \u003cp\u003e\u003cem\u003emunicipality\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5706%;\"\u003e\n \u003cp\u003e\u003cem\u003eShape file\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e\u003cem\u003e_\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5085%;\"\u003e\n \u003cp\u003e\u003cem\u003eTo identify the main service area\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.47458%;\"\u003e\n \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7966%;\"\u003e\n \u003cp\u003e\u003cem\u003etown boundary\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2429%;\"\u003e\n \u003cp\u003e\u003cem\u003eBOFED,2020\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5706%;\"\u003e\n \u003cp\u003e\u003cem\u003eShapefile\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5085%;\"\u003e\n \u003cp\u003e\u003cem\u003eFor study area boundary delineation.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 8.47458%;\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.7966%;\"\u003e\n \u003cp\u003e\u003cem\u003ePopulation density\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2429%;\"\u003e\n \u003cp\u003e\u003cem\u003eHossana ,CSA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5706%;\"\u003e\n \u003cp\u003e\u003cem\u003eExcel \u0026nbsp; \u0026nbsp; data\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4068%;\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.5085%;\"\u003e\n \u003cp\u003e\u003cem\u003ePopulation density map\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis Techniques\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlanning the location of a facility requires consideration of a number of important factors, including available space, measurement criteria, demand points, and possible service centres(KABITE et al. 2012). In order to maximise service coverage or minimise response time and expenses, the best places for service centres must be identified, and their assignment to demand points must be decided (Davoodi, 2019). In the case of fire station location planning, several factors must be considered, including population density, proximity to critical infrastructure, topography, and existing resources. This process involves modeling, formulating, and solving location-based problems to determine the best locations for emergency services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFire Station Location Planning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor effective service delivery, emergency employees must respond quickly after an incident is reported. This requires ensuring that fire stations are strategically located to minimize response times and cover high-risk areas effectively(Twigg 2017).The fire station location planning process in this study follows these steps:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. Determining the criteria for selecting the best fire station locations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Creating a database model for each criterion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3. Converting the datasets into a suitable format (raster format).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4. Assigning weights to each criterion using the Analytical Hierarchy Process (AHP).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5. Identifying the most optimal site for a fire station.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermining the criteria for the optimal fire station location\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study identifies and defines five critical criteria based on available data and existing literature that are relevant to the study area. These criteria include:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistance to Service Area:\u003c/strong\u003e The fire station location should minimize travel time to key service areas to ensure quick response during emergencies.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003ePopulation Density:\u003c/strong\u003e Areas with high population density should be prioritized to ensure that emergency services reach the greatest number of people within the shortest time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSlope:\u003c/strong\u003e The station must be located on level ground to ensure efficient access to high-risk areas and avoid complications due to steep terrain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Drainage:\u003c/strong\u003e Good drainage conditions are crucial for emergency access and water availability for firefighting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExisting Fire Stations:\u003c/strong\u003e The location of existing fire stations is an important factor to ensure that new stations are well-distributed and can cover underserved areas.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eDistance to service area \u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFire stations should be situated to efficiently serve the neighbourhood and guarantee accessibility in a timely manner. To determine the ideal locations for the fire station, the distance between possible sites and important service regions was computed and examined using GIS technologies.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003ePopulation density \u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAn Excel document containing Hosanna\u0026rsquo;s population data was used to construct population density maps that prioritized locations with a higher risk of fire. With a goal of limiting emergency vehicles\u0026apos; travel time to no more than five minutes, this map assisted in identifying locations for the fire station.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eSlope \u0026nbsp;\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eSlope ArcGIS 10.8 was used to analyses slope data that was collected from the Alaska Satellite Facility (ASF). In order to prevent fire stations from being situated in locations that would make it difficult to reach them in an emergency, the land was divided into four distinct slope groups according to how steep they were.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003e\u003cstrong\u003eDrainage\u003c/strong\u003e \u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDrainage The ALOS DEM was used to analyses drainage conditions, which are essential for battling fires. Drainage density was then assessed using GIS hydrological techniques. Drainage density influences the choice of fire station locations by providing insight into flood risk and water availability for combating fires.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003e\u003cstrong\u003eExisting fire station\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn order to determine underserved areas, the location of existing fire stations was taken into consideration. To make sure that new fire stations are positioned strategically for the best coverage, GIS data on the locations of these stations was integrated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical hierarchy process\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA popular multi-criteria decision-making technique that assists in selecting the best option from a range of options based on a number of criteria is the Analytical Hierarchy Process (AHP) (Kalamaras et al., 2000). Using Saaty\u0026apos;s scale and pairwise comparisons, AHP was used in this work to assign weights to the selection criterion (Table 2). To rank possible locations according to their suitability for the location of fire stations, global weights were computed after local weights for each criterion were determined.As a multi-criteria decision-making (MCDM) method, AHP was originally developed by Saaty (Saaty, 1990, 1977, 1986) and involves pairwise comparisons to establish rankings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeight Determination Using AHP\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;In order to determine the relative relevance of each criterion using Saaty\u0026apos;s scale, the first step in implementing AHP is to compare the criteria pairwise (Table 2). Following the completion of the pairwise comparisons, the local weights were determined using the results. To ascertain the overall significance of each alternate location, these weights were subsequently normalized and displayed in a final matrix.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRate of Consistency and Thresholds\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;the consistency ratio (CR) was computed to assess the pairwise comparisons\u0026apos; consistency. Comparisons are deemed consistent if CR is less than 0.1 (Saaty, 1990). Inconsistencies in the judgements were resolved by rewriting the paired comparisons if CR was greater than 0.1. This stage guarantees the validity of the decision-making procedure and the final weights.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Sensitivity\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;sensitivity study was conducted to evaluate the potential effects of changes in the allocated weights on the final site selection, given the subjectivity of AHP in assigning weights. This research guarantees that the selected fire station location is stable across various weight distributions and helps determine how sensitive the results are to shifts in expert opinions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProposing Suitable Sites for Fire Station Location\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA final suitability map showing the best places for fire stations was created by integrating the data using ArcGIS after the AHP approach was used and the criteria were given weights. Since Ethiopia lacks a national standard for the placement of fire stations, the analysis took into account the weights given to each criterion to make sure the sites chosen satisfy the unique requirements of the research region as well as pertinent international norms.\u003c/p\u003e\n\u003cp\u003eTable 2: Saaty\u0026rsquo;s scale\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7561%;\"\u003e\n \u003cp\u003eRANK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2439%;\"\u003e\n \u003cp\u003eDESCRIPTION\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7561%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2439%;\"\u003e\n \u003cp\u003eEqually importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7561%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2439%;\"\u003e\n \u003cp\u003eModerate importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7561%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2439%;\"\u003e\n \u003cp\u003eStrong importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7561%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2439%;\"\u003e\n \u003cp\u003esignificant importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49.7561%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50.2439%;\"\u003e\n \u003cp\u003eExtremely importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3: Consistency of random matrices\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"654\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2956%;\"\u003e\n \u003cp\u003eMatrix order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80092%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80092%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.49464%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.026%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.026%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8729%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.683%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2956%;\"\u003e\n \u003cp\u003eRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80092%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.80092%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.49464%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.026%;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.026%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8729%;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.683%;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eWith the completion of the data analysis, it is imperative to examine and carefully discuss the methods used for data analysis, as well as the result achieved. The processed data was displayed using tables, maps, and graphs to illustrate the outcomes. Initially, all criteria maps were created following the methodology outlined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparation Criteria Map for Each Factor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReclassified Slope layer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs stated earlier, the slope of the study region was obtained from the PLASAR ALOS DEM, which features a resolution of 12.5 meters, using ARCGIS 10.8. The slope degree indicates how elevation varies across the landscape, where lower slope values signify flat areas, and higher values denote steeper terrain. In this research, the slope angle was categorized following the ERA (2013) classification into four groups: gentle (flat) slopes (0\u0026deg; - 2\u0026deg;), rolling slopes (2\u0026deg; - 14\u0026deg;), mountainous slopes (14\u0026deg; - 37\u0026deg;), and escarpments (\u0026gt;37\u0026deg;). The analysis indicated that gentle slopes, with angles under 2\u0026deg;, comprise 31.66% (11.65 square kilometres) of the overall area. Moderately steep or rolling slopes, ranging from 2\u0026deg; to 14\u0026deg;, encompass 36.213% (13.49 square kilometres) of the region, while mountainous slopes represent 24% (8.94 square kilometres) of the total area. Figure 1 shows the reclassified slope layers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo establish new fire stations, regions with flat or rolling slopes are considered appropriate due to their relatively gentle terrain, which aids in easier construction and ensures a rapid emergency response. In contrast, mountainous and escarpment zones, with steeper slopes are deemed less favourable for new fire station sites. Given the challenging terrain, these locations would incur significantly higher construction costs and pose obstacles to quick emergency response. Table 4 and Figure 3 display the reclassified slope layer. Fire spread is influenced by slope; steeper slopes cause fire to spread more quickly because of convection and fuel preheating (Rothermel, 1972). According to studies, fire spreads almost twice as quickly on slopes higher than 30\u0026deg; as it does on flat ground (Alexander, 1982). By Scott \u0026amp; Reinhardt\u0026apos;s (2001) findings, steeper slopes were given higher weights because of the greater risk of fire propagation. Slope values were grouped according to fire behavior models, with slopes higher than 20\u0026deg; being categorized as high risk (Finney, 1998).\u003c/p\u003e\n\u003cp\u003eTable 4: Reclassified Slope layer\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.04225%;\"\u003e\n \u003cp\u003eS/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3177%;\"\u003e\n \u003cp\u003eSlope degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5775%;\"\u003e\n \u003cp\u003esuitability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003ePercent (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.04225%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3177%;\"\u003e\n \u003cp\u003e0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5775%;\"\u003e\n \u003cp\u003eHighly suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003e19.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.04225%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3177%;\"\u003e\n \u003cp\u003e2-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5775%;\"\u003e\n \u003cp\u003esuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003e13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003e36.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.04225%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3177%;\"\u003e\n \u003cp\u003e14-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5775%;\"\u003e\n \u003cp\u003eModerately suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003e11.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003e32.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.04225%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3177%;\"\u003e\n \u003cp\u003e\u0026gt;37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29.5775%;\"\u003e\n \u003cp\u003eunsuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003e4.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0313%;\"\u003e\n \u003cp\u003e10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eReclassified Existing Fire Location\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuffer analysis was performed around the current fire station locations, with buffers at one square kilometre, two square kilometres, three square kilometres, and four square kilometres utilized to evaluate the effectiveness of fire station coverage and the necessity for more stations. The findings suggest that the one square kilometre buffer, encompassing 7.38 square kilometres or 19.9% of the overall area, is inadequate and does not warrant the establishment of further fire stations, as the present coverage is satisfactory. The two square kilometre buffer, covering 13.72 square kilometres and representing 36.99% of the total area, offers slightly improved coverage but still falls short of fully addressing the need for additional fire stations. Conversely, the examination of the three square kilometres and four square kilometre buffers, which encompass 11.979 square kilometres (32.29% of the total area) and 4.008 square kilometres (10.8% of the total area) respectively, highlights regions that are presently unreachable by existing fire stations. These distances underscore significant coverage gaps, signalling a clear requirement for more fire stations to be located in these areas. The three square kilometre and four square kilometre buffers have been recognized as vital regions where new fire stations would improve emergency response. Figure 4 depicts the reclassified existing fire station locations.\u003c/p\u003e\n\u003cp\u003eThe proximity of fire stations significantly impacts emergency response time and fire containment efficiency. NFPA (National Fire Protection Association) guidelines recommend that urban fire stations be within 1 to 3 km of high-risk areas for optimal response (NFPA 1710, 2020). Weight Justification: Areas farther from fire stations were given higher risk weights, consistent with urban fire safety standards (Gonz\u0026aacute;lez-Olabarria et al., 2012). Classification Justification: Buffer zones were defined based on response time efficiency, where distances beyond 3km were classified as high-risk (Hassan et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReclassified population densities Map\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing ArcGIS 10.8, the population density map was classified into four groups, with Class 1 being the highest population density and Class 4 the lowest. Class 1 encompasses 52.26% of the entire 19.38 km\u0026sup2; area, according to the data, indicating areas with the highest population density. The necessity for a greater concentration of fire stations in densely populated kebeles to enable efficient emergency response is supported by fire responders\u0026apos; oral reports, which indicate a higher incidence of fire occurrences in these locations.\u003c/p\u003e\n\u003cp\u003eIn contrast, regions with moderate to low population densities are represented by Class 3 (3.95 km2) and Class 4 (3.98 km2). The need for fire stations is anticipated to be lower in these areas because there have been less documented fire incidents. Furthermore, 9.78 km\u0026sup2; of the entire area is categorised as sparsely populated, indicating that fewer fire stations may be required because of the decreased risk of fire outbreaks.\u003c/p\u003e\n\u003cp\u003eWhile these findings indicate a correlation between population density and fire risk, the lack of documented historical fire incident data limits a precise validation of this relationship. Future studies should incorporate quantitative fire incident records to strengthen the analysis and compare alternative fire station placement models. This analysis emphasises the importance of strategically placing fire stations in areas with higher population density while considering additional factors such as accessibility and historical fire trends to enhance emergency response efficiency. Figure 5b shows the reclassified population density map. Because of increased ignition sources and infrastructural vulnerabilities, densely inhabited areas are more likely to have fires (Syphard et al., 2007). Research has shown that the frequency of fires and urban density are strongly correlated (Chuvieco et al., 2010).Following fire risk models utilised in urban hazard assessment, high-density areas were given higher weights (Cardille et al., 2001).Fire danger studies were used to determine population density limits, with locations with more than 5000 persons per km\u0026sup2; being categorized as extremely high risk (Modugno et al., 2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Reclassified Service Area Map\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ideal sites for new fire stations were identified by examining how close they are to crucial service areas in the study region, such as police stations, hospitals, schools, marketplaces, and residential neighbourhoods. Using ArcGIS, these service areas were combined and then buffered at intervals from 500 meters to 3 kilometres, following guidelines found in existing literature. The buffering analysis was divided into four categories to evaluate the suitability of fire station placements. Class 1, which refers to a 500-metre buffer, is viewed as the most appropriate, as it reflects a close distance to vital services, allowing for rapid emergency response. Class 2, characterized by a 1-kilometre buffer, and is also considered suitable but not as optimal as Class 1. Class 3, associated with a 2-kilometre buffer, and is regarded as less suitable because the greater distance from service areas might result in longer response times. Lastly, Class 4, indicating a 3-kilometre buffer, is seen as unsuitable for the location of fire stations due to the considerable distance from key services, which could impede efficient emergency response. Consequently, the analysis advocates for prioritizing fire station sites within shorter buffer distances to enhance effectiveness and coverage. Figure 6 illustrates the reclassified service area map. The fire service area was determined based on fire station coverage and accessibility. Studies on fire station accessibility emphasize that response time is a critical factor in reducing fire damage (Kule et al., 2020). Higher weights were assigned to underserved regions, following spatial analysis techniques used in fire station optimization (Gibbons et al., 2018). Service areas were categorized into low (within 3 km), moderate (3\u0026ndash;5 km), and high-risk zones (beyond 5 km), aligning with NFPA and urban planning standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReclassified Drainage Densities Map\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn determining the best sites for new fire stations, drainage densities of the region were assessed using the ALOS DEM data analysed through spatial tools in ArcGIS. This process was initiated by developing a stream order map, followed by the construction of a drainage density map utilising line density execution methods. The resulting drainage densities were divided into four separate categories, each reflecting different levels of drainage density within the study region, as shown in Figure 7.\u003c/p\u003e\n\u003cp\u003eClass 1 encompasses regions with the lowest drainage densities, covering 15.18 square kilometres, which represents 40.9% of the overall study area. These locales, marked by a scarcity of drainage lines, are regarded as highly favourable for the establishment of new fire stations. The limited presence of drainage lines in these zones indicates a diminished risk of flooding, an essential factor for maintaining the accessibility and operational effectiveness of fire stations.\u003c/p\u003e\n\u003cp\u003eClass 2, spanning 10.14 square kilometres or 27.34% of the study area, also displays relatively low drainage densities. While their drainage networks are somewhat more extensive than those of Class 1, these regions remain appropriate for fire station siting due to their manageable risk levels and accessibility. They strike a commendable balance between coverage requirements and risk management, making them a feasible secondary choice.\u003c/p\u003e\n\u003cp\u003eOn the other hand, Classes 3 and 4 show elevated drainage densities, occupying 8.05 square kilometres (21.67%) and 3.72 square kilometres (10.049%) respectively. These areas are less ideal for fire station placement because of the increased complexity and density of drainage systems, which could result in heightened flooding risks and challenges in accessibility, especially during emergencies. The elevated drainage density in these classes indicates a greater susceptibility to water accumulation and other hydrological complications, which could hinder the efficiency of fire station operations.\u003c/p\u003e\n\u003cp\u003eIn summary, the analysis distinctly shows that regions with lower drainage densities, particularly those belonging to Class 1, are the most appropriate for new fire station placements. Class 2 regions are usable as well, though with slightly higher risk considerations. Conversely, areas categorized in Classes 3 and 4 should be steered clear of due to the potential complications arising from their elevated drainage densities. This strategic method guarantees that new fire stations are situated in locations that enhance accessibility while minimizing environmental hazards, thus improving the overall effectiveness of emergency response services. The impact of water bodies and drainage systems on fire occurrence has been extensively documented in wildfire management studies (Tedim et al., 2016). Areas within 200m of drainage channels were classified as low-risk zones, in accordance with recommendations from fire risk zoning studies (Modugno et al., 2016). Areas near drainage were given lower weights due to their role in fire suppression, which is consistent with findings by Keeley et al. (2011). Drainage networks can serve as natural firebreaks, limiting spread and reducing fire intensity (Pyne et al., 1996).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical hierarchy process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Analytic Hierarchy Process (AHP) Consistency Ratio (CR) in this study was 0.008, which is well below the permissible threshold of 0.1 and shows that the pairwise comparisons done during the analysis are very consistent. When the CR value is less than 0.1, it indicates that the decision-makers\u0026apos; decisions were logically sound and free of major inconsistencies. This validates the validity and reliability of the AHP model and guarantees that the criteria prioritization and decision-making process are reliable and consistent (Hillier et al., n.d.,2021). The impact of water bodies and drainage systems on fire occurrence has been extensively documented in wildfire management studies (Tedim et al., 2016). Areas within 200m of drainage channels were classified as low-risk zones, according to recommendations from fire risk zoning studies (Modugno et al., 2016). Weight Justification: Areas near drainage were given lower weights due to their role in fire suppression, which is consistent with findings by Keeley et al. (2011). Drainage networks can serve as natural firebreaks, limiting the spread and reducing fire intensity (Pyne et al., 1996).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProposed a suitable site for fire location\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA suitability map was methodically prepared through the AHP (Analytical Hierarchy Process) extension tool, integrating five weighted parameters to measure the need for new fire stations within the area. The weights allocated to each parameter were grounded on different international standards, a systematic literature review, and the nature of the study area.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5: Weight assigned to criterion\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eCRITERION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eWEIGHT (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eRANK\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePopulation Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eService Area Map\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eExisting Fire Stations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eDrainage Density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe parameters and their respective weights were as shown in Table 4 above.\u003c/p\u003e\n\u003cp\u003eThe analysis outcomes deliver critical insights into the region\u0026apos;s fire station needs. It was resolute that 3.21 square kilometres, creating approximately 9.16% of the total area, are in serious need of a new fire station. Additionally, areas covering 13.37 square kilometres and 13.91 square kilometres, which account for 37.9% and 38.8% of the area, respectively, have been identified as requiring fire stations. These regions have been classified into three categories: Class 1, indicating an immediate need; Class 2, indicating a moderate need; and Class 3, indicating a lower priority.\u003c/p\u003e\n\u003cp\u003eFurthermore, the analysis established that 5.29 square kilometres of the total area, representing 14.74%, fall under Class 4. These areas are already adequately covered by existing fire stations, indicating no necessity for additional stations in these zones. This inclusive assessment highlights the importance of deliberate planning in augmenting fire safety infrastructure across the region. Figure 8 shows a suitability map for the fire station\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProposed Number of New Fire Locations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA suitability map was developed to identify areas that are in need of fire station service based on designated criteria. This suitability map was converted into vector format, and the highest priority areas (Class 1), demonstrating regions with the highest need for fire stations, were extracted. A grid index with a 3 km distance was created using the Grid Index Features, a Data Management tool in ArcGIS. This grid was used for the Class 1 vector data, and a fishnet analysis was then conducted to generate point data. These points represent potential locations for fire stations within the identified high-need areas. The created grid index and fishnet are shown below in Figure 9. This process results in a set of specific locations where fire stations should be established to serve the community effectively based on the selected criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Final proposed fire location\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter creating point data using the fishnet technique, the study area\u0026rsquo;s kebeles in Hossana town were overlaid with the grid index and fishnet results. This overlay analysis allowed the identification of the required number of fire station locations within each kebele. The investigation results, which detail the number of essential fire station locations for each kebele in Hossana town, are illustrated in the map below. Based on the analysis, the considered positioning of fire stations was identified as a critical factor, together with population density and proximity to service areas such as hospitals, residential, and commercial zones. This supports findings from other studies, including Wright et al. (2020), which emphasised the significance of locating fire stations near high-risk and densely populated areas to increase service effectiveness and ensure immediate response times.\u003c/p\u003e\n\u003cp\u003eThe proposed fire station locations for each kebele are as follows:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eHeto\u003c/strong\u003e: Previously had no fire station; now, one fire station is proposed.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLechamba\u003c/strong\u003e: Previously had no fire station; now, one fire station is proposed.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eArada\u003c/strong\u003e: Formerly had one fire station serving the entire town; now, two fire stations are proposed.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSeche Duna\u003c/strong\u003e: Previously had no fire station; now, three fire stations are proposed based on standard requirements.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNaramao\u003c/strong\u003e: Previously had no fire station; now, one fire station is proposed.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBobocho\u003c/strong\u003e: Previously had no fire station; now, one fire station is proposed.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTable 6, summarises the number of fire stations before and after the proposal for each kebele in Hossana town.\u003c/p\u003e\n\u003cp\u003eTable 6: Proposed fire location for each Kebele in Hossana Town\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7204%;\"\u003e\n \u003cp\u003ekebele\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.853%;\"\u003e\n \u003cp\u003eFire Stations Before\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4265%;\"\u003e\n \u003cp\u003eProposed Fire Stations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7204%;\"\u003e\n \u003cp\u003eHeto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.853%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4265%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7204%;\"\u003e\n \u003cp\u003eLechamba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.853%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4265%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7204%;\"\u003e\n \u003cp\u003eArada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.853%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4265%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7204%;\"\u003e\n \u003cp\u003eSeche Duna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.853%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4265%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7204%;\"\u003e\n \u003cp\u003eNaramao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.853%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4265%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.7204%;\"\u003e\n \u003cp\u003eBobocho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.853%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39.4265%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy using multi-criteria decision analysis and GIS technologies, this study sought to determine the best places for fire stations in an urban setting. By integrating engineering, environmental, and social factors into a unified spatial framework, it improves upon existing urban planning techniques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptimal fire location\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the analysis, the most important determinant of appropriate fire station placements is population density. This is in line with research by Johnson \u0026amp; Lee (2021) and Smith et al. (2019), who highlighted that locations with a high population density create more emergency demand and so need priority service coverage. The study adds to the generalizability of this criterion in fire station layout by confirming this link in a new geographic setting. Furthermore, the study shows that the second most important factor is closeness to vital service sectors, such as residential neighborhoods, schools, hospitals, and existing fire infrastructure. This bolsters Wright\u0026apos;s (2018) claim that improving service responsiveness and reducing response time requires proximity to critical and vulnerable infrastructure. The necessity of integrated urban infrastructure design is further supported by this conclusion, which also demonstrates that fire station layout cannot be divorced from larger urban service distribution patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of Environmental and Engineering Parameters.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis includes environmental factors, including slope and drainage density, in contrast to earlier models that mostly focused on population and accessibility. Although frequently disregarded, these elements have a big impact on how well emergency services operate. For instance, in an emergency, vehicles may find it difficult to access regions with poor drainage or steep slopes. By taking these limitations into account, the study guarantees risk-aware infrastructure development and long-term functional viability in addition to suggesting ideal sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScientific Contribution and Broader Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethodologically, a flexible and reproducible model for multi-criteria spatial decision-making is provided by the combination of AHP and SMCDA. The structure of the model improves transparency and permits scenario-based testing, even though the application of AHP adds subjectivity to the weighting process. This method adds to the body of literature on urban planning by showing how spatial analytics can be used to organize and assess expert knowledge. Crucially, the approach takes into consideration fairness (making sure under-represented regions are covered) and efficiency (reducing response time), a balance that is frequently absent from traditional fire station layouts. Data-driven investment in emergency services is supported by the geographical findings of this study, which directly feeds policy by indicating priority areas for new infrastructure.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cstrong\u003eIdentification of optimal location\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study shows that combining AHP with SMCDA offers a transparent and methodical approach for determining the best places for fire stations. The approach facilitates well-informed decision-making that goes beyond instinctive or politically motivated siting by taking into consideration variables like population density, fire risk, accessibility, and environmental limits. The findings support more equitable emergency response planning by showing that the suggested locations greatly improve coverage in high-need areas and decrease service inequities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Current Fire Stations\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;There are significant discrepancies between the sites of current fire stations and high-risk and densely populated areas, according to a spatial analysis of existing stations. This research identifies areas with inadequate service as well as those where station location is not strategically in line with modern urban reality. Some stations are misaligned and could need to be moved, while others need to be upgraded. These findings imply that maintaining operational success requires regular reevaluations of station locations based on spatial criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eService Area Analysis and Coverage Improvements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed station network offers more equal and comprehensive geographic coverage, especially in formerly underserved neighborhoods, according to the service area study. The optimized configuration increases access for at-risk populations and reduces projected response times when compared to the current structure. In addition to being operationally important, these results also advance more general objectives of public safety and urban resilience.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEnvironmental and Cost Considerations\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;the methodology encourages sustainable design by incorporating environmental and financial assessment into the site selection procedure. Locations that avoid environmentally sensitive areas, use pre-existing infrastructure, and save construction costs were prioritized. This method offers a reproducible paradigm for sustainable emergency facility planning in urban settings while reducing long-term operational and environmental constraints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Reduction and Accessibility\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;High accessibility is emphasized by the optimized station location method, especially when it comes to areas that are prone to fire and road networks. The system's capacity to react swiftly to crises is improved by this geographical prioritization, especially in places that are susceptible. As a result, the approach encourages fire response infrastructure to transition from reactive to proactive planning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the model offers a well-organized and useful framework, there are still certain restrictions. Although useful for multi-criteria decision-making, the AHP method includes subjectivity through expert judgement in weight assignment. The temporal accuracy of the model may also be impacted by the lack of real-time fire incident data. Subsequent investigations ought to examine the incorporation of dynamic data sources, such as past fire logs and current sensor data, and assess the framework's applicability in various urban settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendation\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eIntegration of Geographic Information Systems (GIS) in Firefighting Site Selection: When choosing a firefighting site, urban planners, legislators, and designers ought to consider Geographic Information Systems (GIS). With the use of extensive geographical analysis made possible by GIS, the best locations are found by taking into account social and environmental aspects.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Critical Elements to Take Into Account When Choosing a Firefighting Location: When choosing a firefighting location, planners should take into account a number of elements, such as population density, proximity to high-risk fire zones, accessibility, infrastructure, and the environmental impact. To guarantee optimum effectiveness and coverage, these elements ought to direct the best possible placement of firefighting services.\u003c/li\u003e\n \u003cli\u003eEvaluation of Current Firefighting Sites: It is critical to evaluate fire stations' existing sites regularly to ensure they still serve the community's needs and the appropriateness of their current locations.\u003c/li\u003e\n \u003cli\u003eDeveloping New Firefighting Locations with the Help of Geospatial Analysis. Advanced geospatial techniques should be used to propose new places when it is determined that the current locations are insufficient. By using this strategy, it will be possible to ensure that the new locations are positioned to offer improved coverage in areas that are prone to fires and quicker reaction times.\u003c/li\u003e\n \u003cli\u003eDevelopment of Strategic Policies Based on Scientific Research: When developing regulations meant to lower the risk of fire, policymakers should take this study's conclusions into consideration. The strategic location of firefighting stations should be the main focus of these strategies, with data-driven station placement that satisfies both urban and rural safety regulations.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding Declaration:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eEthics, Consent to Participate, and Consent to Publish Declarations\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eData Availability Declaration\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;the data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting Interest Declaration\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;the authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAleisa, Esra. 2018. \u0026ldquo;The Fire Station Location Problem: A Literature Survey.\u0026rdquo; \u003cem\u003eInternational Journal of Emergency Management\u003c/em\u003e 14(3): 291.\u003c/li\u003e\n \u003cli\u003eAmlashi, B Azmoudeh. 2019. \u0026ldquo;The Role of Optimal Site Selection of Fire Stations in Urban Safety.\u0026rdquo; 4(3): 223\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003eAyele, Leykun Getaneh, Yirga Kebede Wondim, and Abiyu Demessie Abebe. 2018. \u0026ldquo;GIS Based Suitable Site Selection and Road-Map Preparation for Equitable Distribution of Secondary Schools of Amhara Region ,.\u0026rdquo; \u003cem\u003eJournal of Environment and Earth Science\u003c/em\u003e 8(1): 100\u0026ndash;113.\u003c/li\u003e\n \u003cli\u003eBadri, Masood A., Amr K. Mortagy, and Colonel Ali Alsayed. 1998. \u0026ldquo;A Multi-Objective Model for Locating Fire Stations.\u0026rdquo; \u003cem\u003eEuropean Journal of Operational Research\u003c/em\u003e 110(2): 243\u0026ndash;60.\u003c/li\u003e\n \u003cli\u003eDavoodi, Mojtaba. 2019. \u0026ldquo;A GIS Based Fire Station Site Selection Using Network Analysis and Set Covering Location Problem (Case Study: Tehran, Iran).\u0026rdquo; (December 2018): 433\u0026ndash;36.\u003c/li\u003e\n \u003cli\u003eErden, T, and M Z Cos. 2010. \u0026ldquo;Multi-Criteria Site Selection for Fire Services : The Interaction with Analytic Hierarchy Process and Geographic Information Systems.\u0026rdquo; (1980): 2127\u0026ndash;34.\u003c/li\u003e\n \u003cli\u003eErden, Turan, and Mehmet. 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Geospatial technology provides strong decision support for pinpointing these ideal locations. However, the use of geospatial technology for planning fire locations has not been widely investigated in Ethiopia. This research aims to create an effective strategy for fire location selection in Hosanna Town by utilizing advanced geospatial methods. This study used ALOS DEM, demographic data, and comprehensive field assessments to determine the key elements that impact the selection of fire locations. The Analytical Hierarchy Process (AHP) was used to give weights to these elements, which consist of slope, drainage density, population density, distance to service areas, and distance to existing fire stations. By applying the AHP method, thematic maps for each of these factors were created, which were subsequently merged through a weight overlay technique to identify the most suitable sites for new fire stations. At present, the current fire facilities cater solely to a single kebele, revealing a noteworthy gap in coverage. To remedy this deficiency, the study advises the creation of nine additional fire stations in line with literature and international benchmarks. Ultimately, the use of geospatial tools in planning fire locations has shown to be very effective. The research emphasizes the importance of integrating these technologies for enhanced site selection and infrastructure development, advocating for their incorporation into future fire risk management plans","manuscriptTitle":"Developing Optimal Firefight Station Using Geospatial Techniques: A Case Study of Hosanna Town","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 01:47:18","doi":"10.21203/rs.3.rs-5571915/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":"2a48155a-5db4-4ca0-ae8d-46bd8cd9ffeb","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-06T18:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-26 01:47:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5571915","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5571915","identity":"rs-5571915","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}