Groundwater artificial recharge indexing using fuzzy catastrophe membership functions

Water shortages have resulted from the unsustainable exploitation of aquifers, the increased need for agricultural and drinking water, the pollution of surface water resources, and reduced water resources. Replenishment of groundwater resources through artificial or natural recharge (from rainfall and runoff) is one of the ways to compensate for this issue. The data layers used in site selection for Groundwater artificial recharge (GWR) are heterogeneous and, therefore, cannot be directly integrated. Catastrophe Fuzzy Membership (CFM) functions are among the latest advances in this field, making it possible to integrate various types of data layers. However, the type of catastrophe function and fuzzy membership intervals are determined based on expert opinion. This study determined the final weights of criteria and sub-criteria, and 16 indicators and 76 sub-criteria were selected to evaluate potential sites for artificial recharge in Tabriz Plain, Iran. The results showed that the areas with gentle slopes in the center of the study area have great potential for groundwater recharge, while the mountainous areas in the north and South are unsuitable. The final suitability map was created using remote sensing (RS) and Geographic Information System (GIS) software.