# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 4 Artificial Neural Networks Models for Water Quality Prediction
[4] proposed an improved method of ABC (IABC) which added the optimal and global optimal solution to the updated formulas. The result indicated that the limitation of the method above was that water quality data needed to obey the normal distribution appropriately. [PERSON] et al. [54] used sparse auto-encoder (SAE) to pre-train the hidden layer data because SAE contained deep latent features. [PERSON] et al. [58] determined the structure of DBN by growing and pruning algorithms instead of artificial experience (SODBN). Results showed that SODBN could short running time and improve accuracy. [PERSON] and [PERSON] [57] applied Adam optimization method which could handle sparse gradients on noisy problems to train the parameters of CNN. [PERSON] et al. [151] focused on the Improved Grey Relational Analysis (IGRA) method which calculated the similarity and proximity by relative area change ratio. [PERSON] et al. [5] used variational mode decomposition (VMD) algorithm to decompose the highest frequency component produced by a complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN). ELM was applied for modeling. Results indicated that this hybrid model could reduce error whether in root mean square or mean absolute error. [PERSON] et al. [152] proposed an improvement variant namely improved genetic algorithm (IGA) to avoid the situation where excellent individuals are discarded by the GA. [PERSON] et al. [15] introduced evidence theory, that has good data fusion ability, since it is able to reason with uncertainty to synthesize the evidence from SRU, Gated Recurrent Unit (GRU), LSTM sources in DO, pH, TP prediction, and eventually reached a certain level of belief. The improved probability assignment function of the evidence theory, designed based on the softmax function, could solve the failure of weight allocation problems existing in the traditional probability assignment function. As a general framework of uncertain reasoning, the application of evidence theory can be further extended. [PERSON] et al. [153] combined transfer learning (TL) and ANNs approaches which do not require a large amount of training data because TL has the ability to transfer knowledge from past tasks to predict Chl-a dynamics. The biggest difference between TL and traditional ANNs methods is that the former does not need to learn each task from scratch while the latter does. Results indicated that the hybrid models enhanced the generalization ability compared with the dropout and parameter norm penalties methods in the long-term application. At the same time, the impact of mutable data distribution on the models was decreased. [PERSON] et al. [154] utilized mean value method using a median of k data before and after to correct wrong data and got the missing data by the values of model prediction of other water quality variables at the missing point. The restricted condition of the model was that the data were appropriately and normally distributed. Therefore, it is uncertain whether the above method can be applied to other prediction tasks that do not meet the above conditions. [PERSON] et al. [68] proposed a hybrid optimized algorithm, namely PSO and GA, to optimize BPNN with reasonable accuracy. [PERSON] [45] investigated the DO prediction, which considered a temporal and spatial relationship. Spatial relationship refers to the spatial correlations between external variables instead of the geographic distributions. The newly proposed attention-RNN model achieved excellent performance whether in short-term and long-term prediction. [PERSON] et al. [63] tested the performance of decomposition approaches, DWT and VMD, in DO prediction. They concluded that these two methods are an alternative tool for accurate prediction when the input was combination III and model was MLP.