Key points are not available for this paper at this time.
To improve water quality, total daily loads must be established, and this requires determining the quality of the water in rivers, storage tanks, ponds, and coastal areas. Current methods to evaluate water quality involve the collection of water samples for subsequent laboratory analysis. Although these technologies offer precise measurements for a specific location and time, they are expensive, time-consuming, and do not provide the continuous, temporal, or spatial conditions of water quality that are required for managing, assessing, and monitoring water quality. In order to calculate the water quality, the water quality index is modeled using artificial neural network models that incorporate feedforward neural network backpropagation neural networks and radial neural networks. The water quality index of Malaysia’s Klang River was determined by training the artificial network using six major sub-quality parameters. Compared to the current method, the artificial neural network simplifies and expedites the computation of the water quality index. The artificial neural network method could provide a significant saving in terms of money and time while offering a robust assessment of water quality. The proposed method could also be used as an early warning system for pollution of water bodies. The best artificial neural network was the feedforward neural network with one hidden layer containing 5 neurons. Furthermore, conventional approaches for calculating the water quality index rely on empirical equations, often introducing a high degree of approximation and uncertainty into the results. Moreover, these equations cannot be applied when some parameters are not measured. In contrast, the artificial neural network methods and technique offer an efficient and straightforward process for estimating and creating prediction models for water quality index. Doi: 10.28991/CEJ-2024-010-07-012 Full Text: PDF
Ismail et al. (Mon,) studied this question.