Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

The economic development, livelihood and drinking water of millions people in the central plateau Iran depend on Qarah-Chay River, but due to a lack inappropriate monitoring, it has been exposed destruction pollution. Consequently, an assessment river’s quality is utmost importance for both management human health maintenance safe environment, which can be achieved by determining best locations pollution monitoring stations along rivers. In this study, artificial neural networks (ANNs) used optimize River Markazi province, Iran. data are collected based Iranian Water Quality Index (IRWQI), US National Sanitation Foundation (NSFWQI) Oregon (OWQI). database given multilayer perceptron (MLP) network with geographic information system (GIS). output study identified six river, mainly downstream accumulation land uses concentration gradient MLP training courses model from proposed 0.062299. addition, performance evaluation criteria F1-score, recall, precision accuracy were 0.85, 0.84, 0.88 0.88, respectively. results obtained help managers properly monitor resources accuracy, efficiency lower cost; furthermore, findings able provide scientific references river ecosystem protection.