A Hybrid Fuzzy Wavelet Neural Network Model with Self-Adapted Fuzzy c-Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers

• Published in: Complexity (New York, N.Y.) Vol. 2018; no. 2018; pp. 1 - 11
• Main Authors: Ying, Guangguo, Kong, Shaofei, Ruan, Jujun, Cai, Jiannan, Yi, XiaoHui, Zhang, Chao, Liu, Hongbin, Tian, Di, Huang, Mingzhi, Zhang, Tao
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Analysis; Artificial neural networks; Chemical oxygen demand; Clustering; Computer simulation; Data mining; Environmental management; Fuzzy logic; Genetic algorithms; Localization; Machine learning; Management; Mathematical models; Methods; Neural networks; Parameters; Performance prediction; Rain; Rivers; Sensors; Software; Variations; Water; Water quality; Water temperature; Wavelet transforms; South China Sea; China; Pearl River
• ISSN: 1076-2787; 1099-0526; 1099-0526
• DOI: 10.1155/2018/8241342

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network (FWNN) including the neural network (NN), the fuzzy logic (FL), the wavelet transform (WT), and the genetic algorithm (GA) was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network (FNN), the wavelet neural network (WNN), and the neural network (ANN). The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river.