Groundwater Potential Zone Mapping: Integration of Multi-Criteria Decision Analysis (MCDA) and GIS Techniques for the Al-Qalamoun Region in Syria

Abstract

One of the most critical processes for the long-term management of groundwater resources is Groundwater Potential Zonation (GWPZ). Despite their importance, traditional groundwater studies are costly, difficult, complex, and time-consuming. This study aims to investigate GWPZ mapping for the Al-Qalamoun region, in the Western part of Syria. We combined the Multi-Influence Factor (MIF) and Analytic Hierarchy Process (AHP) methods with the Geographic Information Systems (GIS) to estimate the GWPZ. The weight and score factors of eight factors were used to develop the GWPZ including drainage density, lithology, slope, lineament density, geomorphology, land use/land cover, rainfall, and soil. According to the findings, about 46% and 50.6% of the total area of the Al-Qalamoun region was classified as suitable for groundwater recharge by the AHP and MIF methods, respectively. However, 54% and 49.4% of the area was classified as having poor suitability for groundwater recharge by the AHP and MIF methods, respectively. These areas with poor suitability can be utilized for gathering surface water. The validation of the results showed that the AHP and MIF methods have similar accuracy for the GWPZ; however, the accuracy and results depend on influencing factors and their weights assigned by experts.

GIS-based multi-criteria decision making for delineation of potential groundwater recharge zones for sustainable resource management in the Eastern Mediterranean: a case study

“…Marine sandstone and Sediment have been given the highest weightage for maximum groundwater permeability, followed by the rest of the features (Table 7). Fieldwork revealed that the riverine and marine sedimentary environment gives greater water recharge to the aquifer, and this is consistent with many related studies (Barua et al 2021;Alrawi et al 2022;Ghadeer 2022).…”

“…Groundwater in Syria is one of the crucial foundations for supporting the process of economic and social development, especially in the post-war phase (Baba et al 2021;Alrawi et al 2022). The development of a multi-criteria spatial model that enables reliable demarcation of regions constitutes an urgent research gap at the level of creating flexible and sustainable groundwater management strategies in Syria in the face of current and future challenges.…”

Insights of dam site selection for rainwater harvesting using GIS: A case study in the Al- Qalamoun Basin, Syria

“…The artificial neural network (ANN) and fuzzy rule-based (FR) models require a substantial amount of input prediction data [ 67 ], such as the location of dam sites and ponds, in order to accurately predict rainwater harvesting (RWH). Conversely, multi-criteria decision analysis (MCDA) models, including the analytic hierarchy process (AHP), do not rely on input data as they utilize logical classification of factors to assign weights and ratings to input parameters for the purpose of delineating the RWH map [ [68] , [69] , [70] ]. In this study, the Analytic Hierarchy Process (AHP)-based MCDA [ 71 ] and GIS-based Soil Conservation Service Curve Number (SCS–CN) were employed to generate a potential RWH map of the study area.…”

Proposing Optimal Locations for Runoff Harvesting and Water Management Structures in the Hami Qeshan Watershed, Iraq

“…For instance, vegetation cover influences various water cycle processes: runoff, evapotranspiration, and infiltration [110,111]. Alrawi [112] reported that agricultural lands decelerate surface runoff and increase water infiltration. The LC map of HQW was provided by GEOSURV (Figure 9a).…”

“…One of the essential procedures in evaluating the accuracy of any model is the validation of results, where models might not be advantageous from a scientific perspective without verification [112]. In reviewing the literature, different methodologies are utilized to validate the RH maps, such as correlation analysis, segmentation accuracy assessment, receiver operating characteristic curve, and sensitivity analysis [4,20,31,44,56].…”