A Hybrid Method for Designing Sustainable River Monitoring Networks Using Fuzzy Logic Site Selection and Genetic Algorithm Optimization

Water is a vital resource for safe life, and effective monitoring programs are essential to manage floods, droughts, and the misuse of water resources. Surface water monitoring networks require strategically placed stations; hence, critical factors must be considered for their positioning. Following the destruction of the previous river monitoring network in the Lorestan province in 2018, the Kashkan River catchment basin was selected for this study. A topological data model was employed. Twelve criteria were identified for station locations, and the fuzzy logic site selection method was used to determine the best locations. Different weightings were applied to each criterion, depending on the monitoring purpose, and a hierarchical analysis method was used to achieve this objective. Then, a two-objective genetic algorithm optimization model is used to propose a monitoring network of potential stations. Modeling was done for weight coefficients between 0.01 and 100, and the weight coefficient range of 2 to 6 was determined to be appropriate for balancing cost and accuracy. The results were analyzed using the maximum entropy method with the help of the MaxEnt model. This research presents a hybrid method for designing a river monitoring network. Unlike previous studies, this method allows for applying any design criterion suitable for monitoring without needing historical data. With this method, not only will the network have proper accuracy, but it can also be designed according to the project budget.