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

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 7327072 - 23
• Main Authors: Su, Xuehua, He, Xiaolong, Zhang, Gang, Chen, Yuehua, Li, Keyu
• Format: Journal Article
• Language: English
• Published: United States Hindawi 28.04.2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Back propagation networks; Efficiency; Environmental law; Environmental management; Fuzzy logic; Kernel functions; Management; Neural networks; Optimization; Optimization algorithms; Population; Prediction models; Search algorithms; Support vector machines; Viscosity; Water; Water quality
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/7327072

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 R2 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.