Application of Artificial Neural Network to Predict Total Dissolved Solid in Achechay River Basin
Saeed Kanani, Gholamreza Asadollahfardi, Ali Ghanbari Sorkhi · 2008
River water salinity is a significant concern in many countries, considering agricultural and drinking usages. Therefore, prediction of amount of salinity is a necessary tool for planning and management of water resources. Since Achechay River Basin in East Azerbaijan province in Iran passes through saline zones, use of the water for irrigation has become problematic. In this regard, prediction of future salinity of Vaniar station in Achechay river basin was studied using Artificial Neural Network (ANN) with a month time delay as predictor, considering the effect of discharge with 24 hours time delay and Total Dissolved Solid (TDS), TDS monthly mean data and daily mean discharge for thirty years are considered as inputs for the ANN and TDS is the output of the models. Multi Layer Perceptron (MLP) and Input Delay Neural Network (IDNN) methods were applied to the data. The results of the study showed that predictions of river salinity using Artificial Neural Network are reasonable, suitable and of acceptable accuracy. Hence, prediction of water salinity by ANN may be useful for water quality planning and management.