Optimal water quality monitoring network during road construction using Bayes and Entropy theories

The spatial variation of road construction runoff, and environmental impacts on both the terrestrial and aquatic environment necessitate the monitoring of receiving water quality. The paper proposed an integrated methodology for spatial optimization of the water quality monitoring network (WQMN) using information-theoretic techniques, including the concepts of the Transinformation entropy (TE) and the value of information (VOI). First, based on the correlation analysis, the most significant water quality parameters were selected. Then, using the Canadian Council of Ministers of the Environment (CCME) method, the water quality index (WQI) was computed in each potential monitoring station. After that, the VOI and TE for all potential stations were calculated. To achieve an optimal network among potential stations, the NSGA-II multiobjective optimization model was developed considering three objective functions, including i) minimizing the number of stations, ii) maximizing the VOI in the selected network, and iii) minimizing TE by the selected nodes. The optimization model resulted in a set of optimal solutions for WQMNs, called Pareto-front. Finally, two multi-criteria decision-making models including TOPSIS and PROMETHEE were utilized for choosing the best solution on the Pareto-front space considering various weighing scenarios assigned to objectives. The applicability of the presented methodology was assessed in a WQMN of a road construction site (33 km) in E18 highway, south of Norway. The selected solutions by TOPSIS and PROMETHEE models present the WQMN with maximum VOI and minimum TE among 33, and 28 potential stations, respectively.