Modeling groundwater vulnerability prediction using geographic information system (GIS)-based ordered weighted average (OWA) method and DRASTIC model theory hybrid approach

摘要

A groundwater vulnerability prediction modeling, based on geographic information system-based ordered weighted average (OWA)-DRASTIC approach, is investigated in southern part of Perak, Malaysia. The proposed approach is a mix of curiosity that allows the uses of different decision strategies for the purpose of quantifying level of risk in vulnerability prediction. Seven pollution potential factors based on DRASTIC model theory were individual evaluated. Their results were model using OWA generic model. The OWA model integrates a pair-wise comparison method and quantifier-guided OWA aggregation operators to form a groundwater pollution potential mapping method that incorporates different decision strategies. With OWA operators, ANDness, ORness, and Trade-off parameters were calculated as a function of fuzzy (linguistic) quantifiers. The calculated parameters lies between the aggregations that uses "AND" operator (which requires all the criteria to be satisfied) and OR operator (which requires at least one criterion to be satisfied). The model results in multiple groundwater vulnerability prediction scenarios, which apply different decision strategies and provide users with the flexibility to select one of them based on the level of risk controls in decision-making process. The risk adverse model associated with OWA AND operator was selected for groundwater vulnerability prediction map in the area. The results showed that predominant portions of the area belonged to the no vulnerable zones. The model was validated with groundwater quality data, and results show a strong relationship between the groundwater vulnerability model and pH, NO3, Ca, Fe, and Zn concentrations whose correlation coefficients are 0.50, 0.55, 0.60, 0.69, and 0.91, respectively. The results obtained confirmed that the methodology hold significant potential to support the complexity of decision making in evaluating groundwater pollution potential mapping in the area.