Research on SVR Water Quality Prediction Model Based on Improved Sparrow Search Algorithm

Abstract

Multiparameter water quality trend prediction technique is one of the important tools for water environment management and regulation. This study proposes a new water quality prediction model with better prediction performance, which is combined with improved sparrow search algorithm (ISSA) and support vector regression (SVR) machine. For the problems of low population diversity and easily falling into local optimum of sparrow search algorithm (SSA), ISSA is proposed to increase the initial population diversity by introducing Skew-Tent mapping and to help the algorithm jump out of local optimum by using the adaptive elimination mechanism. The optimal values of the penalty factor C and kernel function parameter g of the SVR model are selected using ISSA to make the model have better prediction accuracy and generalization performance. The performance of the ISSA-SVR water quality prediction model is compared with BP neural network, SVR model, and other hybrid models by conducting water quality prediction experiments with actual breeding-water quality data. The experimental results showed that the prediction accuracy of the ISSA-SVR model was significantly higher than that of other models, reaching 99.2%; the mean square deviation (MSE) was 0.013, which was 79.37% lower than that of the SVR model and 75% lower than that of SSA-SVR model, and the coefficient of determination R 2 was 0.98, which was 5.38% higher than that of the SVR model and 7.57% higher than that of the SSA-SVR model, indicating that the ISSA-SVR water quality prediction model has some engineering application value in the field of water body management.

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

“…The forecasting performance of SVR is mainly affected by C and gamma (Ngo N et al, 2022), so we used optimization algorithms to optimize these two parameters. Based on previous literature and the performance of the three optimization algorithms in this paper, for fairness, we have the same settings for all three algorithms (Lu et al, 2022;Mohammed S et al, 2022;Sharma and Shekhawat et al, 2022;Su X et al, 2022): iterations: 30; population: 20; and lower and upper bound [0.1,1]. In the Bagging algorithm, the main factors that affect its performance include base learners and data samples (Mohammed and Kora et al, 2023).…”

Prediction of chlorine residual in effluent based on SSA-SFA-CFBLS for water treatment process parameters

“…This approach overcomes the limitation of a single prediction model by considering both linear and non-linear features, which can improve the prediction precision. Su et al [7] proposed an SVR water quality prediction model based on an improved sparrow search algorithm (SSA). This method uses kernel technique [8] to process nonlinear data and solves the problem that the kernel function [9] of SVR is difficult to determine.…”

An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

“…Support Vector Regression (SVR) is a kind of machine learning model [14], based on the principle of statistical learning, which is more suitable for dealing with complex nonlinear relationships, thus replacing the traditional multiple regression method, and has been widely researched and applied in water quality prediction. Su et al [15] combined the Improved Sparrow Search Algorithm (ISSA) and SVR to predict water quality parameters, and employed ISSA to select the penalty factor c and kernel function parameter g of SVR to improve the accuracy and generalization ability of the model, and obtained better prediction results.…”