Prediction of Water Quality Parameters Using an Artificial Neural Networks Model

Land use and cover (LULC) play crucial roles in driving water quantity and quality processes in watersheds. Often changes in LULC have direct effect on water quality of downstream waters. Therefore, developing relationships between LULC and water quality parameters is essential for the evaluation of surface water resources should the LULC change. In this paper we present a methodology based on Artificial Neural Networks (ANN) to predict water quality parameters in ungauged basins; Chlorine (Cl), Sulfate (SO₄), Sodium (Na), Potassium (K), Dissolved Organic Carbon (DOC). The model relies on LULC percentages, temperature, and flow discharge as inputs. The approach is tested on 18 watersheds in west Georgia varying in size from 296 to 2659 ha. Total number of data for each parameter is 801 ranging from 15 to 54 from 18 watersheds. Out of 18 watersheds, 12 were selected for training, 3 for validation and 3 for testing the ANNs model. Each set of validation and testing data consists of 1 forested, 1 pastoral, and 1 urban watershed while training data consist of 7 forested, 3 pastoral, and 2 urban watersheds. The model performance was measured with coefficient of determination (R²), Nash- Sutcliffe efficiency coefficient (E), and bias ratio (R_B). The model developed using the training data set has successfully predicted the water quality parameters in the independent testing watersheds. The coefficient of determination (R²) in the test watersheds ranged from 0.64 to 0.99 while E ranged from 0.54 to 0.98. Results from this study indicates that if water quality and LULC data are available from multiple watersheds in an area with relatively similar physiographic properties, then one can successfully predict the impact of LULC changes on water quality in any watershed within the same area.