Predicting the Effects of Hinosan Toxin on the Water Resources Using Modeling by Artificial Neural Network: A Case Study of Gilan Province, Iran

Underground resources are especially vital as one of the most important sources of supplying and transporting water consumed in the industry, agriculture and civil sectors and it has great quality fluctuation because it passes through various beds and regions,. Most existing models about predicting and simulating the current and future conditions of quality status of underground waters require numerous input parameters to which access is difficult or the measurement of which require high cost and time. In this research, the performance of Artificial Neural Network (ANN) algorithm has been evaluated to predict the effects of Organophosphorus Hinosan on the underground waters of Gilan province, Iran. The used data is related to rural underground water resources in different regions of Gilan state includes substance of land, distance from agriculture lands, depth of underground well, pH, electric conductivity, salinity and precipitation amount as input parameters and concentration of Hinosan as output parameter. The obtained results of this evaluation indicate high precision of neural networks in predicting the effects of Hinosan toxin on the quality of underground water of Gilan province which could be a good model for most managerial decisions.