# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 6 Discussion  
### Data Are the Foundation  
Data selection strategy: Data collection is a costly and time-consuming process. This is mainly due to the expensive equipment, limited experimental time, and conditions. The ANN model is a data-driven model, so obtaining enough data is the basis of modeling. The need to collect as much data as possible has been put forward in the existing literature. To address this need, researchers need to consider two factors. One is whether the historical information of the output variable can be collected. The second is what strategy researchers need to choose when the historical information of output variables are difficult to measure. If researchers can collect historical observations of target variables, they can process the data and model it. If target variables cannot be obtained, researchers can collect variables such as water quality and meteorology data associated with output variables. Part of the literatures collect target variables by means of obtaining open-source data. This approach has benefited from a number of government data collection programs. However, research to obtain open-source  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Methods** & **Comments** \\ \hline \multirow{8}{*}{Deterministic} & BP algorithm(L) & Computing the direction of gradient descent \\  & [PERSON]’s methods(L) & The computing tasks are implemented by Hessian matrix \\  & Conjugate gradient & The search direction is carried along the conjugate \\  & method(L) & direction and does not need to use Hessian matrix \\  & Levenberg–Marquardt & A method, combination of BP and Newton algorithm, \\  & method(L) & use Jacobian matrix to do the computing tasks \\  & The Quasi-Newton & It is applied to the situation of that Jacobian matrix or \\  & method(L) & Hessian matrix is difficult or even impossible to compute \\  & BFGS & A Quasi-Newton method implemented by the built-in \\  & TRAINLM & A gradient descent with momentum and \\  & Global optimization & & See Table 9 \\ \hline \multirow{2}{*}{Stochastic methods} & Bayesian methods & Prediction limits can be obtained \\  & [PERSON] optimization method & It implemented a reverse gradient update with the value \\  & & obtained by Mini batch data \\ \hline Emerging methods & Online learning algorithm & Quickly adjust the model in real time \\ \hline \hline \end{tabular}
\end{table}
Table 10: The deterministic and stochastic methods in model training.  
water quality data is rather limited. Therefore, researchers are encouraged to open up their own data resources in the future to make contributions to themselves, others, and society.  
Data volume demand: According to the results of the existing literature review, there is no systematic research to investigate how to determine the optimal number of samples required for each type of ANN model. In general, RNN requires more data than feedforward artificial neural networks. In addition, GRNN in feedforward artificial neural networks can handle small sample problems. When researchers use the RNN method to make water quality predictions, they need about a thousand pieces of data. When the researchers utilized the feedforward artificial neural networks method to make the prediction, about 500 pieces of data are needed. When researchers used the GRNN method to make predictions, they need about 100 pieces of data. When researchers want to make long-term forecasts of water quality data that are periodic after a year, at least one year of data needs to be collected. This also applies when researchers want to include four seasons in the model validation and testing phase.