# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 6 Discussion
### Data Processing Is Key  
Data imbalance problem: Both the peak and the extreme value occupy a relatively small proportion of the distribution. Only a handful of researchers currently consider data imbalances. In order to obtain higher prediction accuracy and reduce the error of the peak, some new prediction approaches, such as wavelet analysis method, can be used for reference. Besides, modelers in the future can develop a form of extreme value loss for detecting the future occurrence of extreme values ([PERSON] et al., 2019) and apply it to the water quality prediction. Input selection problem: The quality of data sets has been affected by many factors. These factors include but are not limited to temporal resolution (e.g., monthly vs. hourly), number of predictors, or noise in the data. Therefore, it is very important to select the appropriate input and preprocess the data. This review found that the vast majority of researchers chose inputs based on their domain knowledge or in any arbitrary manner. Such input selection methods have some limitations because they neither analyze the relationship between input and output, nor consider the performance of the model. Some studies use cross-correlation to explore the relationship between inputs and outputs. It is a linear approach, which is contrary to the premise of using a nonlinear neural network model. Researchers can use nonlinear analysis methods such as mutual information to select inputs. Output strategies problem: A variety of output strategies were adopted in the reviewed papers--the quantity of which is 18--because researchers hope to select the most suitable through comparison to illustrate the relationship between input(s) and output(s), which is good practice. Multivariate-Input-Other (multi)-Output is the most popular output strategy which represents the case where the output(s) at a specific point is learned the historical information from other variables (more than one). Few studies have considered the spatial relationship between exogenous variables. This may be due to the fact that external variables do not influence the outcome of the forecast most of the time. However, researchers must be aware that exogenous variables can have a significant impact on predictions at some point. For example, the effect of water circulation on dissolved oxygen. A recent research used the mechanism of attention to simultaneously explore the relationship between temporal and spatial, and applied it to DO prediction. Researchers can use this method for reference to further explore the spatial relationship of other water quality variables. Forecasting length problem: At present, the research mainly focuses on the short-term prediction, and the research on the long-term prediction is relatively limited. The reason for this phenomenon is that with the increase in the prediction length, the uncertainty factors also increase, which leads to the accumulation of errors and thus reduces the accuracy of the prediction. Researchers can adopt appropriate strategies to solve such problems in forecasting field, such as Recursive, DirRec, and Multiple Output Strategies ([PERSON], 2018). Data dividing problem: At present, researchers tend to use ad-hoc method to divide the training set data into 70% to 90% of the total data. The most common percentage of the training, validation,and testing is 60%, 20%, 20%, and 50%, 25%, 25%. Such methods based on the expertise of researchers or divide data in arbitrary ways has certain universality. However, this approach has not promoted the development of data partitioning methods. It is always difficult to determine the number of K for common K-fold cross-validation, as the results may have a considerable bias [169]. Therefore, leave-one-out cross-validation, the most extreme form of K-fold cross-validation, should be encouraged for use because it has been shown to provide a good estimation of the model's true generalization capabilities in the case of fewer training data or more model parameters despite the limited usage.