# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 4 Artificial Neural Networks Models for Water Quality Prediction
[PERSON] [142] utilized neural differential evolution (NDE) models, a combination of neural networks and differential evolution approaches, to model SS. The result showed that NDE has a low mean square error. [PERSON] [71] investigated the performance of ARIMA-ANN in WT, DO, and B prediction. [PERSON] and [PERSON] [143] combined PSO and ANN methods in water quality parameter prediction. [PERSON] et al. [1] used cross-correlation and mutual information to select the input to achieve the prediction of BOD and DO, respectively. The conjugate gradient algorithm was carried out to train the model. [PERSON] et al. [144] presented two cluster technique, namely K-means, fuzzy c-means (FCM) in DO prediction. Results indicated that the performance of hybrid methods was better than single models. [PERSON] [64] designed a missing-refilling scheme which divided the data into incidental missing (ID) and structural missing (SD). Then, a temporal exponentially moving average was applied to fill the missing data. They investigated the time relationship of the DO, NH\({}_{3}\)-N univariate time series using a bootstrapped wavelet neural network (BWNN). [PERSON] and [PERSON] [42] used the databases of the European Statistical Office and World Bank to complete the BOD prediction. [PERSON] and [PERSON] [43] integrated k-means clustering and MLP in daily COD concentration modeling by using SS, pH, and WT. Result indicated that this hybrid methods performed better than MLP, RBFNN, and two different ANFIS approaches (subtractive clustering and grid partition). [PERSON] et al. [120] collected 23 water quality parameters and considered the problems of data dimensionality. Therefore, the PCA techniques was used to compress the original data into 15 aggregative indices. Then, the GA approach was applied to optimize the parameters of BPNN. The result showed that the average prediction accuracy was up to at least 88%. [PERSON] et al. [145] developed a data mining method, namely re-sampling, to solve the unbalance problem. [PERSON] [146] recommended collecting more than one-year water quality data, because they wanted to include all four seasons in the validation and testing phases. [PERSON] et al. [147] proposed a hybrid model, namely empirical mode decomposition (EMD)-BPNN. BPNN predicted each sub-series which are IMFs and the residue decomposed by EMD. The results demonstrated that a hybrid model could capture the non-stationary characteristics of WT after EMD. [PERSON] et al. [44] scaled the datasets between -1 and 1 and then used phase space reconstruction (PSR) of chaos theory to extract much more information from BOD datasets. Results showed that the hybrid model, namely chaos theory-PCA-ANN, had high prediction accuracy. [PERSON] et al. [73] applied early stopping which is fit for small networks and datasets to determine the model structure. [PERSON] et al. [148] utilized 5-fold cross-validation to divide the data and applied RBFNN to fuse data from multiple sensors. The convergence rate and the solution accuracy could be improved through the variant of PSO (IPSO). The comparison of prediction results validated the effectiveness of the hybrid model. [PERSON] et al. [149] converted the signal into an output linear system by the Kalman filter. The result showed that this hybrid method was a good and effective approach to water quality prediction. [PERSON] et al. [69] simulated the nonlinearity of data by the combination of the neural network, fuzzy logic, wavelet transform, and the GA. Results showed that this hybrid model could handle the problems of data fluctuation. [PERSON] et al. [123] adopted the most extreme form of K-fold cross-validation, namely leave-one-out cross-validation to divide the datasets. [PERSON] et al., 2017 [16] divided the dataset into training (98%) and testing sets (2%) and adopted the PSO algorithm to accelerate the training speed of WNN. [PERSON] proved that artificial bee colony (ABC) algorithms were more precise than GA and PSO [150]. [PERSON] et al.