Spatio-Temporal Multi-Criteria Optimization of Reservoir Water Quality Monitoring Network Using Value of Information and Transinformation Entropy

Identifying optimal Water Quality Monitoring Stations (WQMS) with high values of information on the entire reservoir status, instead of all potential WQMS would significantly reduce the monitoring network expenditure while providing adequate spatial coverage. This study presented a new methodology for spatio-temporal multi-criteria optimization of reservoir WQMS based on Value of Information (VOI), Transinformation Entropy (TE), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), and IRanian Water Quality Index (IRWQI). Although, all mentioned methods and concepts are well-known and have been used in water resources management, but their integration into a specific application for spatio-temporal multi-criteria optimization of reservoir WQMS is definitely an innovation and a contribution to improvement of WQMS design. More specifically, maximizing VOI as a decision-makers’ design criteria for optimization of WQMS, and considering spatial and temporal variations of water quality at different reservoir depths are new innovations in this research. The multi-objective optimization model was based on three objectives: 1) minimizing costs; 2) maximizing VOI; and 3) minimizing TE (redundant information). Considering these objectives, the NSGA-II multi-objective optimization method was used to find Pareto-optimal solutions. The most preferable solution was then determined using PROMETHEE multi-criteria decision making method. The proposed methodology was applied to Karkheh Reservoir with more than 5 billion cubic meter capacity and 60 km length that is one of the largest reservoirs in Southwestern Iran, however, the proposed approach has the ability to be generalized for any generic reservoir. Considering equal weights for criteria, PROMETHEE method resulted in 6 optimized WQMS out of 60 potential ones and a period of 25 days for optimal sampling interval. The optimized monitoring stations were mainly located at deep parts where most water quality variations are expected to occur. To show sensitivity of the model to different weights, 4 scenarios with various relative weights were evaluated in the PROMETHEE method. Results indicated that by increasing the weight of the second criterion (maximizing VOI), the number of optimized WQMS increased and the sampling interval decreased.