GIS-based Analytic Hierarchy Process Research Articles

GIS-Based Analytical Hierarchy Process Modelling and Mapping of Erosion Vulnerability in the Coastal Areas of Rivers State, Nigeria

The problem of coastal erosion in rivers State Nigeria is a significant issue that has far-reaching consequences for the environment and local communities. Despite the efforts of previous research there remains a lack of comprehensive understanding of the factors contributing to erosion vulnerability and their relative importance, hindering effective decision-making and management practices aimed at mitigating the effects of coastal erosion in Rivers State. Therefore, this study aimed at a GIS-based analytical hierarchy process modeling and mapping of coastal erosion vulnerability in Rivers State, Nigeria. The objectives are to establish and classify the geophysical factors according to the levels of coastal erosion risk, calculate the reliability index of the classified geophysical factors, determine the coastal vulnerable areas across Rivers State using analytical hierarchical process and to produce a coastal vulnerability index map defining the extent of erosion vulnerability in Rivers State. The methodology comprises of the acquisition of primary and secondary data, image pre-processing, image classification, DEM processing, classification and standardization of factors, development of pairwise comparism, and weighted linear combination analysis. The study revealed three distinct coastal erosion vulnerability zones: high, moderate, and low vulnerability. The high vulnerability zone encompassed a total expanse of 545.29 square kilometers, constituting 6.38% of the study area. In contrast, the moderate and low vulnerability zones covered 1941.33 square kilometers and 6052.51 square kilometers, respectively, making up 22.73% and 70.89% of the total area. Bonny (139.28 sq km) was ranked as the most vulnerable due to its role as an oil and gas hub. Degema (111.28 sq km) ranked second and requires urgent erosion control. Okrika and Andoni (71.73 sq km and 62.20 sq km) were third and fourth respectively. It is recommended that an advocate for the systematic approach to coastal vulnerability zoning be introduced in the study. The categorization of areas into high, moderate, and low vulnerability zones provides a standardized framework for assessing coastal regions' susceptibility to erosion. This approach can be applied to other regions to facilitate consistent vulnerability assessments.

Earthquake Risk Assessment Using GIS-Based Analytical Hierarchy Process (AHP): The Case of Bitlis Province (Türkiye)

The risk level of natural disasters such as earthquakes depends on many factors. Some of these are direct hazards, while others are vulnerability factors that increase the risk. In this regard, risk assessment should be performed by evaluating the hazard and vulnerability factors together. The Analytical Hierarchy Process (AHP) is a powerful tool for co-evaluating such multiple decision criteria. The spatial visualization of the results facilitates the direct assessment of risks as well. In the present study, the seismic risk assessment of the Bitlis province in Eastern Anatolia, which has a high seismic risk, was performed by using the GIS-based AHP method. Among many criteria, six effective criteria on earthquake risk such as seismicity, demographic and topographic criteria were taken into consideration based on expert decision makers. It is concluded that the results obtained from the study were quite successful in terms of determining the seismic risks of the study area. Accordingly, while the risks are high in densely populated settlements with high peak ground acceleration (PGA), the risk decreases according to soil and land use.

Soil erodibility mapping of hilly watershed using analytical hierarchy process and geographical information system: A case of Chittagong hill tract, Bangladesh

Soil erosion across watersheds and river basins is an alarming environmental deterioration process that poses severe risks to hydrological systems, hydrogeochemical processes, agricultural productivity, and the global natural ecosystem. The use of the Analytical Hierarchy Process (AHP) and Geographical Information System (GIS) to assess soil erosivity for the watershed is widely known. This study applied the AHP and GIS to understand the degree of erosivity of the hilly Karnaphuli watershed in Chattogram, Bangladesh. The study used topographical maps, soil maps, and satellite imagery datasets. It implemented the GIS-based AHP and weighted overlay technique to derive eight factors (slope, elevation, Stream Power Index (SPI), Land Use and Land Cover (LULC), curvature, soil, Topographic Wetness Index (TWI), and rainfall. The geological stage of erosion potential was also identified using Digital Elevation Model (DEM) data through GIS-based hypsometric analysis. The findings demonstrated that the eastern and north-western parts are particularly vulnerable to erosion compared to other parts of the study area. The most dominant variables identified to influence the process of soil erosion are slope, LULC, elevation, and SPI. According to the AHP analysis, slope was the most influential factor (26%), followed by LULC (23.8%), elevation (20.3%), and SPI (13.9%) in the soil erosion process, and the geological stage of erosion potential was determined from the hypsometric curve (S-shaped) and hypsometric integral (0.49), which revealed that moderately eroded areas characterized the whole research region. The findings are significant as they provide valuable information for researchers and planners to address soil erosion and develop measures to control it effectively.

Delineation of groundwater potential zone using remote sensing and GIS-based AHP for sustainable groundwater management in Aceh Besar Regency, Aceh Province, Indonesia

Sustainable groundwater management is crucial for ensuring the long-term availability of water resources and supporting socio-economic activities. As water demands increase and hydrological systems become more complex, accurately identifying groundwater potential zones is essential for effective resource planning and utilization. However, the in-situ data availability is often very limited, particularly on a regional scale. To solve this problem, this study aimed to delineate groundwater potential zones in Aceh Besar Regency using remote sensing and a GIS-based AHP (analytical hierarchy process). Several factors were used to determine groundwater potential zones, including lithology, land use/land cover, slope, lineament density, drainage density, rainfall, and soil type. Each factor was weighted based on an extensive literature review and knowledge-driven decision-making techniques. The results indicated that 26.35% of the area was classified as having low groundwater potential, whereas 53.86% was classified as moderate. Areas that have high to very high groundwater potential cover 16.76% to 3.04% of the Aceh Besar territory. The high groundwater potential zones were mostly located in alluvial and clastic sediments lithology, gentle slopes, and had high annual rainfall. Meanwhile, areas with igneous and metamorphic lithology as well as steep slopes, had moderate to low groundwater potential. Validation was carried out by collecting the high-yield existing production wells and calculating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve with relatively good accuracy (74.5%). This research has effectively described the spatial distribution of groundwater potential, contributing to sustainable groundwater management and enabling informed decision-making for sustainable groundwater practices.

Identification of groundwater potential zones using integrated GIS-based analytical hierarchy process and multicriteria decision analysis methods in Jedeb watershed, Ethiopia

Identification of groundwater potential zones using integrated GIS-based analytical hierarchy process and multicriteria decision analysis methods in Jedeb watershed, Ethiopia

Forest Fire Risk Mapping Using GIS Based Analytical Hierarchy Process Approach

Turkiye is located in a region sensitive to forest fires due to the Mediterranean climate, vegetation and topography of the south and west coasts. Every year, forest fires for various reasons cause thousands of hectares of forest area to be burned. Fires damage the ecosystem as well as have economic consequences. While the Mediterranean, Aegean and Marmara coasts, where the Mediterranean climate and fire-sensitive tree species are dominant, are at primary risk against forest fires, the forests located on the Black Sea coasts where the Black Sea climate is dominant are less risky in terms of fire. For an effective fight against forest fires, it is crucial to identify zones with fire risk based on various parameters such as forest structures (tree species, crown closure, stand age), topographic features (slope, aspect), climate, and proximity to certain points (such as roads, settlements, agricultural areas). Fire risk data will shed light on the measures that can be taken against fire. In this study, GIS (Geographic Information Systems) based Analytical Hierarchy Process (AHP) method, one of the well know MCDA (Multi-criteria Decision Making Analysis) methods, was used to develop the fire risk map of Mersin Forestry Regional Directorate (FRD) within the Mediterranean region of Turkiye. Then, the accuracy of the fire risk map was evaluated by taking into account the previous fires in the regional directorate. As a result, it was found that 13.87% of the study area was classified as very high, 25.87% high, 24.68% medium, 22.44% low and 13.14% very low risk areas. The results showed that tree species are the most effective risk factor in forest fires, and followed by stand age factor. The accuracy of the fire risk map was evaluated by using the location information of a total of 562 fires in Mersin FRD between 2003-2022 that damaged forest areas. In order to determine the accuracy of the fire risk map, ROC (Receiver Operating Characteristic) curve method was used in ArcGIS environment. As a result, the AUC (Area Under Curve) value was found to be approximately 74%, and these values showed that the fire risk map developed for Mersin FRD was moderately reliable. With this study, it has been demonstrated that it is possible to produce reliable fire risk maps in a short time using the GIS-based AHP method.

GIS-Based Analytical Hierarchy Process Modeling for Flood Vulnerability Assessment of Communities Along Otamiri River Basin Imo State, Nigeria

In recent times, communities along the Otamiri River Basin in Imo State have been grappling with flooding issues, especially during the rainy season. This occurs despite the presence of underground drainage systems. The primary concern is heavy rainfall causing the river to overflow and lead to flooding. Hence the study aimed at identifying the flood-prone areas in the Otamiri River Basin in Owerri, Imo State. The objectives are to establish factors for evaluating flood vulnerability within the study area; to classify and standardize the factors according to levels of vulnerability; to determine the reliability of the classified factors; and to produce a flood vulnerability map showing vulnerable areas in the study area. The methodology involved collecting Shuttle Radar Topography Mission and Sentinel 2A imagery of July 2022, and processing the data with ArcGIS and QGIS software to determine the topography and vulnerability areas through geo-referencing and classification. The Analytical Hierarchy Process (AHP) model was employed to identify high flood risk areas, considering factors like drainage density, slope, soil type, precipitation, population density, Euclidean distance, and land use. The study's results categorized vulnerability into five levels: Very Low (0.09% of Owerri, minimal risk), Low (12.93% with lower risk), Moderate (68.83% facing substantial risk), High (18.18% with significant risk), and Very High (0.03% posing extreme risk). These findings are recommended as foundational data for future flood studies in the region.

Site suitability assessment for solar power plants in Bangladesh: A GIS-based analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) approach

Solar power plants are important alternatives to fossil fuel-based power plants because they reduce greenhouse gas emissions and mitigate the effects of climate change. The potential of harnessing solar energy is highly dependent on selecting the optimal locations for plant installation. This study primarily aims to select optimal sites for solar energy plant installation using the Analytic Hierarchy Process (AHP) of Multi-Criteria Decision Analysis (MCDA) in a Geographic Information System (GIS) environment. In the underlying processing phase, the AHP method was used to calculate the relative weights of the study area's physiographic, utility, and climate data. These weights were then used in the MCDA to identify the optimal locations for solar plants. The results of the MCDA were presented in the form of a solar plant suitability map, which showed that 44.59 % (66506.49 km2) of the study area in the south and southwest of Bangladesh is highly suitable for the development of utility-scale solar power plants. In contrast, there are substantial areas of about 31.18 % (46507.37 km2) of the country where the installation of any type of power plant is completely prohibited. This research can help authorities and stakeholders decide where to invest in solar energy.

Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process

The use of satellite precipitation products can overcome the limitations of rain gauges in flood hazard mapping for mitigation purposes. Hence, this study aims to evaluate the capabilities of three global precipitation measurement (GPM) integrated multisatellite retrievals for GPM (IMERG) products in tropical flood hazard mapping in the Kelantan River Basin (KRB), Malaysia, using the GIS-based analytic hierarchy process (AHP) method. In addition to the precipitation factor, another eleven factors that contribute to flooding in the KRB were included in the AHP method. The findings demonstrated that the spatial pattern and percentage area affected by floods simulated under the IMERG-Early (IMERG-E), IMERG-Late (IMERG-L), and IMERG-Final (IMERG-F) products did not differ significantly. The receiver operating characteristics curve analysis showed that all three IMERG products performed well in generating flood hazard maps, with area under the curve values greater than 0.8. Almost all the recorded historical floods were placed in the moderate-to-very-high flood hazard areas, with only 1–2% found in the low flood hazard areas. The middle and lower parts of the KRB were identified as regions of “very high” and “high” hazard levels that require particular attention from local stakeholders.

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.