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

Abstract

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.

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

“…We select several existing data-driven prediction methods for WTR: CNN, 46 LSTM, 47 FNN, 35 WFNN. 41 CNN and LSTM respectively denotes standard CNN model and standard LSTM model. FNN and WFNN respectively refers to fuzzy neural network model and wavelet fuzzy neural network model that have been briey introduced in Section 1.…”

“…Besides, Han et al 39 also proposed an improved multiobjective optimal controller related to Qiao et al And to pursue a faster convergence speed of models, wavelet transformation theory is also integrated into neural network model for prediction of WTR. [40][41][42] Loussi et al 40 proposed a hybrid computational strategy that combines kernel methods with fuzzy wavelet network to realize prediction of WTR. Huang et al 41 presented a fuzzy wavelet neural network model for WTP and really accelerates processing speed.…”

“…[40][41][42] Loussi et al 40 proposed a hybrid computational strategy that combines kernel methods with fuzzy wavelet network to realize prediction of WTR. Huang et al 41 presented a fuzzy wavelet neural network model for WTP and really accelerates processing speed. Cong et al 42 exploited adaptive weighted fusion and wavelet neural network model to construct an estimation method for WTR.…”

Fuzzy jump wavelet neural network based on rule induction for dynamic nonlinear system identification with real data applications

“…Also, inappropriate regulation of wavelet parameters reduces the generalizability of the model [13]. Also as another challenge of the FWNN, it is not a simple work to extract effective fuzzy rules [14]. The mentioned challenges are significant problems, especially in practical applications.…”

A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full‐Scale Anaerobic Wastewater Treatment Process

“…Hence, a new system with the fuzzy wavelet neural network (FWNN) was established by integrating advantages of various intelligent techniques. is network could effectively increase the detection rate and reliability of the model by improving the discernment, generalization, and approximation capacities [3,29,30]. Such an integrated intelligent system can overcome the shortcomings mentioned above.…”