Abstract
The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.
Highlights
Increased surface water pollution due to urbanization, excessive water consumptions, population growth, industrial wastewater discharge, and agricultural activities results in low dissolved oxygen (DO) levels and worsens the existence conditions in aquatic systems [1,2,3,4]
Because of the limited water quality data and to overcome the difficulties in DO prediction in near-hypoxic river systems, this study proposes the use of feedforward neural network (FNN) for the prediction of DO in the Nyando River basin. e advantage of FNN is that even with a single hidden layer and arbitrary bounded and smooth activation function, the network is capable of approximating a continuous nonlinear function
The chemical, physical, and biological components of aquatic ecosystems vary and are complex and nonlinear in the relationship. e study results showed that the application of feedforward backpropagation neural networks (FNNs) is an effective approach in the identification and modeling of nonlinear interacting water quality parameters for the prediction of dissolved oxygen in scarcely monitored basins, as compared with the statistical multiple linear regression (MLR)
Summary
Increased surface water pollution due to urbanization, excessive water consumptions, population growth, industrial wastewater discharge, and agricultural activities results in low dissolved oxygen (DO) levels and worsens the existence conditions in aquatic systems [1,2,3,4]. Parametric statistical and deterministic models have been the traditional approaches for modeling water quality, these models require vast information and data on various hydrological subprocesses in order to arrive at the end results [10,11,12]. These models require precisely determined rate constants and coefficients pertaining to various hydrological, chemical, physical, and biological processes, which are largely time and space specific in nature. Complexity though these models have analytical solutions, they have boundary conditions as limitations [13,14,15]
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