# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 4 Artificial Neural Networks Models for Water Quality Prediction  
From 2008 to 2019, the use of the ANN technique has been very popular in the field of water quality prediction. Many researchers have utilized ANNs to model and predict water quality. [PERSON] et al. [75] adopt ANN to predict the BOD, which is difficult to measure and needs at least five days to get the final results in WWTP. Results showed that COD was the most effective variables on BOD estimation after conducting the sensitivity analysis. [PERSON] and [PERSON] [76] revealed that ANN techniques depend on using more input data to solve the water quality problems, although they did not illustrate the size of the appropriate datasets. [PERSON] et al. [40] tested MLP and GRNN models with various input selected by stepwise constructive methods for multistep prediction of S, DO, and Chl-a. They pointed out that the limited data set was one of the drawbacks of their research and encouraged others to collect more data to recalibrate and revalidate the model. [PERSON] et al. [19] employed a typical three-layer of MLP structure [77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89] with the BP algorithm to achieve Chl-a prediction. They divided the dataset into training (75%) and testing parts (25%). Results indicated that ANNs could establish a stable and effective model for Chl-a prediction. This result is also suitable for other parameters  
Figure 5: The architecture of a Convolutional Neural Network. Figure 6: The architecture of deep belief network. prediction. [PERSON] et al. [90] evaluated ANN, MNN, and adaptive neuro-fuzzy inference system (ANFIS) performance in 1-h and 2-h ahead prediction of DO and TOC. They added Q to inputs because rainfall affected the water quality prediction. It was found that using the Levenberg-Marquart algorithm to train the MNN could provide the least error and better results. [PERSON] et al. [91] divided the data into training (60%), validation (20%), and testing sets (20%). They adopted a sensitivity analysis method to find out the important water quality parameters and excluded fewer influence variables, resulting in a compact network. [PERSON] et al. [92] used BPNN to COD and ammonia nitrogen (NH\({}_{3}\)-N) prediction. The whole datasets were normalized at first and then divided into training (80%) and testing (20%) sets. The sigmoid transfer function that can establish the random nonlinear map between inputs and outputs were adopted. [PERSON] et al. [93] divided the data into training (50%), validation (25%), and testing sets (25%). [PERSON] et al. [94] employed a golden section method to select the hidden layer nodes of BPNN. [PERSON] et al. [95] investigated the partition approach in evaluating the relative importance of eleven environmental variables to the output layer. They divided the datasets into training (60%), validation (20%), and testing sets (20%). Results showed that the predicted values of the ANN model were close to the measured value. [PERSON] et al. [96] combined Precip and Q to realize a one-step prediction of Q. Then, the connected system utilized the prediction value of Q and historical TOC to fulfill the one-step prediction of TOC. Finally, the connected system had better performance than a single ANN model. [PERSON] and [PERSON] [97] pointed out that ANN models could process complex and multivariable problems. [PERSON] and [PERSON] [98] verified the feasibility of ANN technique, data-driven models, in predicting DO. Results showed that the ANN method was superior to the nonlinear regression (NLR) technique. [PERSON] et al. [99] scaled the datasets to lie between 0 and 1 [100, 9, 101, 102, 103, 104, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114] so that it could be compatible with the sigmoid transfer functions used in the hidden layer and applied the constructive and pruning of stepwise methods that aim to maximize the model's performance through a constant adjustment to surface water quality prediction. [PERSON] et al.