Abstract
Reliable prediction of water quality changes is a prerequisite for early water pollution control and is vital in environmental monitoring, ecosystem sustainability, and human health. This study uses Artificial Neural Network (ANN) technique to develop the best model fits to predict water quality parameters by employing multilayer perceptron (MLP) neural network and the radial basis function (RBF) neural network, using data collected from three district municipalities. Two input combination models, MLP-4-5-4 and MLP-4-9-4, were trained, verified, and tested for their predictive performance ability, and their physicochemical prediction accuracy was compared by using each model’s observed data with the predicted data. The MLP-4-5-4 model showed a better understanding of the data sets and water quality predictive ability giving an MSE of 39.06589 and a correlation coefficient (R2) of the observed and the predicted water quality of 0.989383 compared to the MLP-4-9-4 model (R2 = 0.993532, MSE = 39.03087). These results apply to natural water resources management in South Africa and similar catchment systems. The MLP-4-5-4 system can be scaled up for future water quality prediction of the Waste Water Treatment Plants (WWTPs), groundwater, and surface water while raising awareness among the public and industry on future water quality.
Highlights
IntroductionWater quality plays an essential role in any aquatic system, such as reflecting the degree of water pollution [1] and influencing the growth of aquatic organisms [2]
The Artificial Neural Network (ANN) model was developed to test its predictive performance on the quality of river water and Waste Water Treatment Plants (WWTPs) and has a great opportunity as a predictive tool
The multilayer perceptron (MLP) 4-5-4 network showed the best predictive ability for water quality. The application of this model to the river basins in the study area has shown the possibility of using available data in a given catchment to predict water quality while recognizing the fact that such data-intensive models as ANN may not be successful in developing countries where data is inadequate, a notable limitation in the present study
Summary
Water quality plays an essential role in any aquatic system, such as reflecting the degree of water pollution [1] and influencing the growth of aquatic organisms [2]. Predicting future water quality changes is a prerequisite for early water pollution control [3] and plays a crucial role in environmental monitoring, ecosystem management, and human health [1]. As influenced by natural and human-induced occurrences [8], the water quality of any catchment serves as scientific evidence for economic development, commercial planning, and water resources protection from future contamination of that catchment [8]. Water quality monitoring and prediction are of utmost importance to public health and are mandatory and crucial for better managing accessible water resources and building up various remediation strategies [9]
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