GIS-based Analytic Hierarchy Process Research Articles

Geospatial Modeling Based-Multi-Criteria Decision-Making for Flash Flood Susceptibility Zonation in an Arid Area

Identifying areas susceptible to flash flood hazards is essential to mitigating their negative impacts, particularly in arid regions. For example, in southeastern Sinai, the Egyptian government seeks to develop its coastal areas along the Gulf of Aqaba to maximize its national economy while preserving sustainable development standards. The current study aims to map and predict flash flood prone areas utilizing a spatial analytic hierarchy process (AHP) that integrates GIS capabilities, remote sensing datasets, the NASA Giovanni web tool application, and principal component analysis (PCA). Nineteen flash flood triggering parameters were initially considered for developing the susceptibility model by conducting a detailed literature review and using our experiences in the flash food studies. Next, the PCA algorithm was utilized to reduce the subjective nature of the researchers’ judgments in selecting flash flood triggering factors. By reducing the dimensionality of the data, we eliminated ten explanatory variables, and only nine relatively less correlated factors were retained, which prevented the creation of an ill-structured model. Finally, the AHP method was utilized to determine the relative weights of the nine spatial factors based on their significance in triggering flash floods. The resulting weights were as follows: rainfall (RF = 0.310), slope (S = 0.221), drainage density (DD = 0.158), geology (G = 0.107), height above nearest drainage network (HAND = 0.074), landforms (LF = 0.051), Melton ruggedness number (MRN = 0.035), plan curvature (PnC = 0.022), and stream power index (SPI = 0.022). The current research proved that AHP, among the most dependable methods for multi-criteria decision-making (MCDM), can effectively classify the degree of flash flood risk in ungauged arid areas. The study found that 59.2% of the area assessed was at very low and low risk of a flash flood, 21% was at very high and high risk, and 19.8% was at moderate risk. Using the area under the receiver operating characteristic curve (AUC ROC) as a statistical evaluation metric, the GIS-based AHP model developed demonstrated excellent predictive accuracy, achieving a score of 91.6%.

GIS-based analytic hierarchy process (AHP) for soil erosion-prone areas mapping in the Bone Watershed, Gorontalo, Indonesia

GIS-based analytic hierarchy process (AHP) for soil erosion-prone areas mapping in the Bone Watershed, Gorontalo, Indonesia

Assessment of land suitability for irrigation in West Shewa zone, Oromia, Ethiopia

The assessment of land suitability is the key to sustained agricultural output. Thus, the study aimed to assess the land suitability for irrigation development in the West Shewa zone, Oromia, Ethiopia. A GIS-based analytical hierarchy process was applied to evaluate a multi-criteria land suitability analysis. The key factors such as soil (depth, drainage, texture, pH, organic carbon, available water content, and salinity), slope, land use/cover, proximity to the river, proximity to the road, proximity to urban areas, and rainfall deficit were considered. These factors were reclassified, weighted, and then overlaid using the weighted overlay tool of ArcGIS software. The study classified the agricultural lands in the area from highly suitable to permanently unsuitable for irrigation to determine the suitability of the classes. The results showed that 10.27% (1419.87 km2) was highly suitable, 73.23% (10,128.97 km2) was moderately suitable, 16.34% (2259.95 km2) was marginally suitable, and 0.16% (22.16 km2) was not suitable. The area in all woredas was mainly moderately suitable for irrigation. However, Metarobi had the most highly suitable land, followed by Elfata with the most moderately suitable land, and Abuna Gindeberet with the most marginally suitable land. The results revealed huge potential for irrigation development in the West Shewa zone. As a result, it can serve as the basis for zonal-level planning and future irrigation development. Therefore, the study helps to improve the community’s lifestyle in the study area by increasing agricultural production.

Snow Avalanche Risk Assessment using GIS-Based AHP for Bitlis Province

Bitlis province, located in the Eastern Anatolia of Turkey, is the region with the highest snowfall in the country. Due to its highland and steep structure, the region is under high avalanche risk. Assessment of the snow avalanche risks is important in terms of modern disaster management. In this study, the avalanche risks were assessed by using the analytical hierarchy process (AHP), which is an effective multiple criteria decision-making methods. The avalanche risk was considered depend on many factors such as temperature, slope, elevation, aspect, land use, soil, lithology, precipitation, distance to fault and population. The outputs obtained from the method were mapped in the GIS environment and thus the avalanche risks of the region were determined. According to the results, especially the highland and steep southern parts and the two volcanic mountain foothills in the region were evaluated as high risk. The study results were validated by comparing past avalanche events and some previous researches.

Marine Suitability Assessment for Mariculture: Combining GIS and AHP Technique in Dampier Strait Conservation Area, West Papua Province, Indonesia

Highlight Research Mariculture is one of the activities that can pressure the aquatic environment. Mariculture activities are carried out by considering the sustainability aspects of environmentally friendly implementation. The suitability of the location for mariculture activities refers to the geographical and hydro–oceanographic conditions seen from the waters' physical, chemical, and biological parameters. The suitability index produced using multiple approaches (GIS-based AHP) can be used to determine the potential locations for mariculture development. Abstract Potential mariculture activities in the Dampier Strait Conservation Area, which are indicated by the availability of water space and local commodities, can have a multiplier effect on socio-ecological aspects. Therefore, reasonable development steps are needed to carry out environmentally friendly aquaculture. This study aims to determine the potential use of water space for the development of mariculture activities based on the level of suitability using multiple approaches. Suitability analysis was carried out with Geographic Information System (GIS) based on the Analytical Hierarchy Process (AHP) by considering the area’s characteristics using several aspects seen from the geographical conditions and water quality (physical, chemical, and biological). Based on the suitability analysis results, the potential water space that can be used for mariculture development activities, namely seaweed and pearl oyster cultivation was 1,130.45 ha (0.49% of the overall space allocation 232,588.59 ha). A justification was then carried out to determine a suitable location in terms of its accessibility and environmental friendliness. The analysis has shown promising results in determining potential locations for developing mariculture activities for seaweed and pearl oyster commodities based on sustainable and environmentally friendly cultivation.

Delineation of groundwater potential zones using remotely sensed data and GIS-based analytical hierarchy process: Insights from the Geba river basin in Tigray, Northern Ethiopia

Study regionThe study was undertaken in the Geba Drainage Basin, major tributary of the Tekeze Drainage Basin in Northern Ethiopia. Study focusThe objective of this study is to identify the groundwater potential zones of Northern Ethiopia's Geba Drainage Basin. Nine groundwater indicators were used as inputs, including lithology, geomorphology, lineament density, drainage density, slope, elevation, land use and land cover, rainfall, and soil. According to their relative influence on groundwater potentiality, the thematic layers and their classes were assigned appropriate weights based on Saaty's 1–9 judgment scale. The assigned weights were then normalized using an Eigen vector-based Analytical Hierarchy process. By superimposing all the normalized layers in the GIS environment, a groundwater potential map was developed. New hydrological insights for the regionFive distinct groundwater potential zones—very good, good, moderate, poor, and very poor were found in the study area. The areal distribution showed that majority of the area (45%) fell within the poor groundwater potential zone. The groundwater potential zones of moderate, very poor, good, and very good cover approximately 27%, 12%, 10%, and 6% of the total area, respectively. The results were compared to the groundwater yield of boreholes collected from the study area. The validation analysis revealed a 72% similarity, which is representative. The employed method is therefore a reasonable option for groundwater development in the Geba Drainage Basin.

Bio-Geophysical Suitability Mapping for Chinese Cabbage of East Asia from 2001 to 2020

The cultivation of Chinese cabbage is a crucial source of daily vegetable supply for both human consumption and livestock feed, particularly in East Asian countries. However, changes in global climate and land usage have resulted in significant shifts in the ecological conditions suitable for Chinese cabbage production, thereby threatening its productivity. To address this issue, this study was conducted to map the bio-geophysical suitability of Chinese cabbage in East Asia (Japan, Northeast China, South Korea, and North Korea) from 2001 to 2020. This study integrated six key factors—temperature, rainfall, photosynthetically active radiation (PAR), soil nitrogen, soil pH, and soil texture—into a seasonal and monthly bio-geophysical suitability assessment using a GIS-based Analytic Hierarchy Process–Multiple-Criteria Decision-Making Analysis (AHP-MCDA). The levels of bio-geophysical suitability were categorized into four levels: optimal, suitable, marginal, and unsuitable. The findings of the study firstly indicate that summer is the optimal season for Chinese cabbage cultivation, as it was found to have the highest level of optimal suitability among the four seasons in East Asia. South Korea has the largest percentage of optimal and suitable areas compared to the other three countries. Secondly, this study also conducted a comparison analysis between bio-geophysical suitability and Normalized Difference Vegetation Index (NDVI) over 20 years, and the results show good consistency between the two indicators, with the highest R2 value being 0.61. Thirdly, the comparison between bio-geophysical suitability and production data in two villages in Japan demonstrates that an increase in suitability from 0.28 to 0.32 indicates a significant increase in production. Production would stay stable even with further increases in suitability. Finally, two case studies with monthly comparisons of bio-geophysical suitability across Japan and East Asia in 2020 provide an effective benchmark for determining optimal sowing and harvest times. This study’s results can provide important insights into the trade of Chinese cabbage and support the development of agricultural insurance programs both for farmers and insurance companies. Furthermore, this approach may also be applicable for the assessment of the suitability of other crops.

Modeling of land suitability for surface irrigation using analytical hierarchy process method in Belessa Districts, northwestern Ethiopia

The objective of this paper was to determine whether the land in Belessa is suitable for surface irrigation. For this, a GIS-based Analytical hierarchy process method was applied. Eight factors/parameters such as soil type, soil depth, soil texture, soil drainage, slope, distance from a water supply, and land cover were used to evaluate the suitability of the area for surface irrigation. The weight of each parameter was calculated using an 8 × 8 pairwise comparison matrix. Then, the land suitability map for surface irrigation was produced by adding weighted parameters using the weighted overlay method under ArcGIS 10.3 software. The land suitability map was divided into four categories: highly suitable (S1), moderately suitable (S2), marginally suitable (S3), and unsuitable (S4 or N1). The results show that 12.2% of the study area is not suitable (S4 or N1) for surface irrigation, whereas 13.9% of the study area is highly suitable (S1) for surface irrigation. Marginally (S3) and moderately suitable (S2) classes covered 26.9%, and 46.9% of the study area, respectively. The accuracy of the model was evaluated by overlaying preexisted irrigation scheme in the study area, which falls under highly and moderately suitable area, confirms that the model is accurate. Therefore, this result will be important to increase crop production in the study area, by implement surface irrigation in highly and moderately suitable lands.

Application of GIS-based analytical hierarchy process for assessment and mapping of flood risk zone in the lower Ramganga River Basin, Western Gangetic Plain, India

Application of GIS-based analytical hierarchy process for assessment and mapping of flood risk zone in the lower Ramganga River Basin, Western Gangetic Plain, India

Land Suitability Planning for Sustainable Mango Production in Vulnerable Region Using Geospatial Multi-Criteria Decision Model

The land suitability in the Ratnagiri district (India) for mango crop has been assessed using a combination of multi-criteria decision making (MCDM) with GIS-based analytic hierarchy process (AHP), and sensitivity analysis. Five criteria are applied in this study to analyze land suitability affecting the mango production, viz., topography, climate, soil properties, soil erosion risk, and soil and water conservation practices, all affecting mango production. To prepare the land suitability maps for the mango plants, weights of criteria were identified through expert opinions and a pairwise comparison matrix. A weighted overlay tool available in ArcGIS software was applied in this study for the weighted overlay analysis. The most sensitive parameters were identified by developing and testing a total of 26 weighting schemes. After analyzing the sensitivity of parameters, the parameters related to soil and erosion such as terracing, contour trenching, stone bund, etc. were found as the most significant factors, before and after implementing the conservation measures. As a result, it was observed in this study that after conservation practices were implemented, the area in the highly suitable (19.4%) and moderately suitable (68.8%) classes was expected to rise, while the area in the marginally suitable (7%) class was expected to decrease. This research revealed that combining MCDM with GIS-based AHP as well as sensitivity analysis techniques increased the reliability of MCDM output for each criterion.

Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria.

Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water resource management and sustainability. In this study, the geographical information system (GIS), analytical hierarchy process (AHP) technique, and machine learning models were integrated to assess and predict the irrigation water quality (IWQ) suitability of the Okurumutet-Iyamitet agricultural-mine district. To achieve this, six water quality criteria were reclassified into four major hazard groups (permeability and infiltration hazard, salinity hazard, specific ion toxicity, and mixed effects) based on their sensitivity on crop yield. The normalized weights of the criteria were computed using the AHP pairwise comparison matrix. Eight thematic maps based on IWQ parameters (electrical conductivity, total dissolved solids, sodium adsorption ratio, permeability index, soluble sodium percentage, magnesium hazard, hardness, and pH) were generated and rasterized in the ArcGIS environment to generate an irrigation suitability map of the area using the weighted sum technique. The derived IWQ map showed that the water in 28.2% of the area is suitable for irrigation, 43.7% is moderately suitable, and 28.1% is unsuitable, with the irrigation water quality deteriorating in the central-southeastern direction. Two machine learning models-multilayer perceptron neural networks (MLP-NNs) and multilinear regression (MLR)-were integrated and validated to predict the IWQ parameters. The coefficient of determination (R2) for MLR and MLP-NN ranged from 0.513 to 0.858 and 0.526 to 0.861 respectively. Based on the results of all the metrics, the MLP-NN showed higher performance accuracy than the MLR. From the results of MLP-NN sensitivity analysis, HCO3, Cl, Mg, and SO4 were identified to have the highest influence on the irrigation water quality of the area. This study showed that the integration of GIS-AHP and machine learning can serve as efficient and rapid decision-making tools in irrigation water quality monitoring and prediction.

Analytic Hierarchy Process (AHP) Based Soil Erosion Susceptibility Mapping in Northwestern Himalayas: A Case Study of Central Kashmir Province

The Kashmir Valley is immensely susceptible to soil erosion due to its diverse topography and unstable geological formations in the Himalayan region. The present study helps in assessing the spatial distribution and prioritizing soil erosion zones in the Central Kashmir region covering the Sindh and Dachigam catchments. The study implemented the GIS-based analytic hierarchy process (AHP) and weighted sum method (WSM) using datasets of precipitation, geological map, soil map, and satellite imagery and derived eleven factors (topographical derivatives, LULC, soil, drainage, rainfall, lithology, wetness index and greenness of an area). The ratings and weightage were proven to be unbiased and reliable based on the observed value of the consistency ratio (CR) (i.e., 0.07). The study depicts 41% of the total area to be extremely vulnerable to soil erosion. The slope varies from 0–62° with mean of 22.12°, indicating 467.99 km2 (26%) and 281.12 km2 (15%) of the area under high and very high susceptible zones, respectively. The NDVI and NDWI maps indicate soil erosion severity covering an area of 40% and 38%, respectively, in highly susceptible zones. High drainage density and curvature zones were observed in 18.33% and 22.64% of the study area, respectively. The study will assist in the planning and implementation of conservation measures.

Likelihoods of Flood Hazards and Risks Using GIS-Based Analytical Hierarchy Process (AHP)

Likelihoods of Flood Hazards and Risks Using GIS-Based Analytical Hierarchy Process (AHP)

Application of GIS-Based AHP Model for the Impact Assessment of COVID-19 Lockdown on Environment Quality: The Case of Kabul City, Afghanistan

The COVID-19 pandemic has negatively impacted the industrial, financial, and social aspects of our daily life due to the implementation of lockdown to protect against the spread of the virus. In addition, the lockdown deduced by COVID-19 has promising positive impacts on air quality and environmental pollution. This study aims to monitor the effects of lockdown on environmental degradation during the pandemic in Kabul city, the capital of Afghanistan, using geospatial data and a statistical model of the Analytical Hierarchy Process (AHP). To achieve the purpose of the study, the most essential influencing factors on air quality were generated from different sources using Google Earth Engine (GEE) and GIS environment; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index NDMI) were calculated using Sentinel-2MSI, Carbon Monoxide (CO) was obtained from Sentinel-5P TROPOMI, and land surface temperature was retrieved from MODIS data. The generated thematic layers (before COVID-19, and during a lockdown of COVID-19) were weighted and rated using the AHP analysis. The weighted layers were spatially overlayed to obtain the final output. Consequently, the environmental quality degradation maps before and during COVID-19 were generated to assess the differences over the 22 districts of Kabul city. The findings of the study show that Kabul city is covered by the very low, low, moderate, high, and very high degradation of the environment by 3.17%, 5.33%, 20.54%, 26.63%, 44.32% before COVID-19 in 201,9 respectively, while the percentages are changed to 4.37%, 8.99%. 16.55%, 37.47%, and 32.62% during the lockdown caused by COVID-19 in 2020. The changes in the percentage of environmental degradation in Kabul city particularly in high and very high zones confirm the positive impact of the lockdown of COVID-19.

Land Suitability for None-Rice Cultivation Areas in Ekiti State Using a GIS-Based Analytic Hierarchy Process Approach

This research aimed at carrying out a land suitability analysis for non-rice cultivation areas in part of Ekiti state using a Geographic Information System based Analytic Hierarchy Process (AHP) approach with a view to ascertaining whether rice can grow in commercial quantities in other Local Government Areas that will lead to improving rice production in the State. To achieve the aim of the research, specific objectives were set, which were to (i) determine the land use/land cover within the study area, (ii) assess the climatic, soil, and topographic factors that pertain to rice cultivation, and (iii) carry out a land suitability analysis on rice cultivation. In this study, the Analytical Hierarchy Process method was used to combine land use/land cover, rainfall, temperature, slope, soil texture, and pH to produce a land suitability map for rice cultivation in non-rice growing areas of the study area. The research result shows that the spatial expanse of land unsuitable for rice cultivation in the non-rice cultivation area of Ekiti state was 1121.6 km2 which translated to 44% of the entire study area. Similarly, areas moderately suitable and suitable for the cultivation of rice were 529.1 km2 (21%) and 899 km2 (35%) of the study area respectively. Furthermore, the outcome of AHP validation showed that the consistency ratio was 0.07, which was less than the specified standard maximum value of 0.1, which signifies that there was a realistic degree of uniformity in the pairwise comparison. Based on the obtained results, it was discovered that: the built-up area in the study was only 5.2%, which signified that the remaining 94.8% of the study area was undeveloped and can serve agricultural purposes in the state. The land suitability map for non-rice cultivation produced for this study was detected to be a true representation of areas suitable for rice cultivation subject to overlay analysis and AHP validation. It was therefore recommended that this study can be used as vital background information for the improvement of rice production in Ekiti State Nigeria.

Development of Flood Hazard Index (FHI) of the Kelantan River Catchment Using Geographic Information System (GIS) Based Analytical Hierarchy Process (AHP)

Kelantan has been facing several cases of catastrophic flooding, causing significant damage to this area. Heavy monsoon rainfall is believed to trigger those floods. This study aims to identify and classify the flood occurrence using the Kelantan River catchment’s flood hazard index (FHI) based on the analytical hierarchy process (AHP). This study developed the FHI using the AHP based on spatial analysis in the geographic information system (GIS) environment. Six physical parameters were selected: annual rainfall, slope, river density, land use and land cover (LULC); elevation; and soil permeability. According to the AHP model, the annual rainfall was the first ranked parameter in terms of importance weight score. Moreover, Tanah Merah and Jeli were the high-risk areas for floods. The present study suggests that the GIS-based AHP method can be highly effective for mapping flood hazards and benefit flood management decision-making.

Flood Risk Assessment Using GIS-Based Analytical Hierarchy Process in the Municipality of Odiongan, Romblon, Philippines

The archipelagic Romblon province frequently experiences typhoons and heavy rains that causes extreme flooding, this produces particular concern about the severity of damage in the Municipality of Odiongan. Hence, this study aimed to assess the spatial flood risk of Odiongan using the analytical hierarchy process (AHP), considering disaster risk factors with data collected from various government agencies. The study employed the geographic information system (GIS) to illustrate the spatial distribution of flooding in the municipality. Sendai Framework was the basis of risk analysis in this study. The hazard parameters considered were average annual rainfall, elevation, slope, soil type, and flood depth. Population density, land use, and household number were considered parameters for the exposure assessment. Vulnerability assessments considered gender ratio, mean age, average income, number of persons with disabilities, educational attainment, water usage, emergency preparedness, type of structures, and distance to evacuation area as physical, social, and economic factors. Each parameter was compared to one another by pairwise comparison to identify the weights based on experts’ judgment. These weights were then integrated into the flood risk assessment computation. The results led to a flood risk map which recorded nine barangays (small local government units) at high risk of flooding, notably the Poblacion Area. The results of this study will guide local government units in developing prompt flood management programs, appropriate mitigation measures, preparedness, and response and recovery strategies to reduce flood risk and vulnerability to the population of Odiongan.

Landslide Susceptibility Mapping in Bitlis Province using GIS-based AHP method

This study presents the landslide susceptibility assessment of the region by considering the landslide-susceptible hazard factors such as slope, precipitation, soil, lithology, distance to the river, land use, elevation, aspect, and distance to active faults as well as historical landslide events and population throughout the province of Bitlis. For this purpose, a GIS-based Analytical Hierarchy Process (AHP) was used as an effective method in multiple decision-making methods. The results showed that approximately 25% of the study area has moderate to high landslide susceptibility. Accordingly, the landslide susceptibility of the study area is high, especially in the southwest and southeast parts of the study area which have mountainous and deep river valleys, and the partially mountainous regions in the north. Compared with previous landslide records and similar susceptibility maps in the literature, the results were found to be quite successful in determining landslide susceptibility of the study area. However, risk assessment wasn’t made within the scope of the study.

Zoning the suitability of the western Mekong Deltafor paddy rice cultivation and aquaculture under current and future environmental conditions.

Ca Mau and Kien Giang, the two provinces of the Mekong Delta bordering the Gulf of Thailand, are facing major environmental challenges affecting the agriculture and aquaculture sectors upon which many livelihoods in this region depend on. This study maps the suitability of these two provinces for paddy rice cultivation and shrimp farming according to soil characteristics and current and future environmental conditions for variables found to significantly influence the yield of those two sectors, i.e., the level of saltwater intrusion, water availability for rainfed agriculture, and the length of the growing period. Future environmental conditions were simulated using the MIKE 11 hydrodynamic model forced by four hydrodynamic scenarios, each one representing different extents of saltwater intrusion during both the dry and rainy seasons, while also considering the availability of water resources for rainfed agriculture. The suitability zoning was performed using a GIS-based analytic hierarchy process (AHP) approach, resulting in the categorisation of the land according to four suitability levels for each sector. The analysis reveals that paddy rice cultivation will become more suitable to Kien Giang province while shrimp farming will be more suitable to Ca Mau province if the simulated future environmental conditions materialise. A suitability analysis is essential for optimal utilisation of the land. The approach presented in this study will inform the regional economic development master plan and provide guidance to other delta regions experiencing severe environmental changes and wishing to consider potential future climatic and sea level changes, and their associated impacts, in their land use planning.

Development and Application of Models for Landslide Hazards in Northern Pakistan

In this paper, new models were investigated and developed for landslide hazards in Muzaffarabad District, located in the Azad Jammu and Kashmir region of Pakistan. The influential factors used in the landslide modelling were land use/landcover (LULC), elevation, slope, slope aspect, rainfall, drainage, road, surface roughness, and topographic index. The GIS-based Analytic Hierarchy Process (AHP) model was applied by utilizing the database of 35 active landslides and their pixels present in classes of all influential factors. The mean landslide hazard values, obtained from the mean landslide hazard analysis, were used as hazard weightages in the AHP model for development of a landslide hazard zone map. The highest mean hazard values for: (i) bare soil in LULC was 14.6%; (ii) 600–800 m in elevation was 6.89%; (iii) 30°–35° in slope was 6%; (iv) S and SW in slope aspect was 9.01%; (v) 1350–1405 mm/yr in rainfall was 9.03%; (vi) 40–80 m in buffered drainage was 12.83%; (vii) 40–80 m in buffered road was 12.48%; (viii) 60–138 in surface roughness index was 10.99%; (ix) −1.74–−1.25 in topographic position index was 13.07%. The percentages of very low, low, moderate, high, and very high landslide hazard zones were 1.48%, 11.80%, 39.36%, 37.36%, and 9.57% respectively. The co-efficient of the determination (r2) value of 0.96 indicated a strong relationship between the model development and validation. Thus, landslide hazard zone map models and methodology indicated a very high accuracy. This landslide hazard zone map could be utilized for the landslide damages’ reduction and the planning and development of road and building infrastructures in the study area. Additionally, this research could be replicated in other landslide prone areas of Pakistan for the minimizing the damages of landslides.