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Neural Network-based Model for Classifying the Water Sources Based on the Physico-Chemical Parameters

Water is considered one of the fundamental and important requirements for world survival. Groundwater is the major water source in Sri Lanka, and most of the domestic and agricultural water requirements are performed based on dug wells since ancient. In addition, over 15,000 tube wells use in the country. Generally, groundwater quality is relatively constant over the year and good. However, the quality of the water is changed based on the chemical parameters of the water. Water quality is one of the major environmental problems in Sri Lanka. Uncontrolled use of groundwater resources becomes a crisis. Because of that, it is most important to manage the groundwater resources for different kinds of uses. Therefore, this study aimed to implement and employ the feedforward Artificial Neural Network (ANN) to determine the exact water source based on the water quality parameters in the Monaragala district. This research attempts to classify water sources using ANN fitted by MLP classifies, Support Vector Machine (SVM), and decision tree model, by taking 20 water quality parameters as input variables. Collected data from the study area, divided as 80% and 20% training and testing the models. The performances of the three models are evaluated using the predicting accuracy, precision value, recall value, R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Performance accuracy of the ANN model pointed as 90%, the SVM model 80%, and the decision tree model 75%. The performance accuracy results present that the ANN can be used for effectively and easily classifying water sources as a viable method.