A Cognitive Approach in Selection of Source for Water Treatment Plant based on Climatic Impact

The importance of finished water quality from water treatment plants (WTP) is manifold due to its direct impact on consumer health. It is observed from recent studies that change in climatic pattern can affect the concentration of various water quality parameters (WQP) at source itself and change in source water quality may adversely affect the efficiency of WTP also. In the present study Analytical Hierarchy Process (AHP) is utilized to prioritize WQP based on its vulnerability towards climate change. A feed-forward, fully-connected, three-layer neural network model is developed to compute the Vulnerability Index (VI) with the priority vectors of the WQP as determined by AHP approach. The model is utilized to predict the overall vulnerability of three different surface water sources namely river water, lake water and wetland water. The proposed AHP-Neural network model shows that River water is least affected due to climatic abnormalities with minimum VI value and may be considered as the best source water for WTP among the three considered alternatives. The sensitivity analysis of the new model is also carried out. Hence, this proposed model may become a reliable tool to the water supply engineers in the coming days of climate change.