Application of Artificial Neural Network and Geographical Information System Models to Predict and Evaluate the Quality of Diyala River Water, Iraq

Abstract

This research discusses application Artificial Neural Network (ANN) and Geographical Information System (GIS) models on water quality of Diyala River using Water Quality Index (WQI). Fourteen water parameters were used for estimating WQI: pH, Temperature, Dissolved Oxygen, Orthophosphate, Nitrate, Calcium, Magnesium, Total Hardness, Sodium, Sulphate, Chloride, Total Dissolved Solids, Electrical Conductivity and Total Alkalinity. These parameters were provided from the Water Resources Ministryfrom seven stations along the river for the period 2011 to 2016. The results of WQI analysis revealed that Diyala River is good to poor at the north of Diyala province while it is poor to very polluted at the south of Baghdad City. The selected parameters were subjected to Kruskal-Wallis test for detecting factors contributing to the degradation of water quality and for eliminating independent variables that exhibit the highest contribution in p-value. The analysis of results revealed that ANN model was good in predicting the WQI. The confusion matrix for Artificial Neural Model (NNM) gave almost 96% for training, 85.7% for testing and 100% for holdout. In relation to GIS, six color maps of the river have been constructed to give clear images of the water quality along the river.