# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 5 Result  
The year of the publication is analyzed at first. Figure 7 plots the number of articles published from 2008 to 2019 each year. There is a growing number of publications since 2008 that use the ANN models to predict the water quality, including above 50% of the papers published since 2015, despite the fact that there are some fluctuations in the quantity of papers--which was in decline in 2010 and 2011. The increasing popularity of ANNs in the field of water resources [155] and environmental engineering [16] may be explained by the major advantage of the ANNs--that researchers can utilize them to model nonlinear and complex phenomena even if they do not fully understand the underlying mechanisms [156]. The popularity of ANNs above is also in agreement with the observations of other researchers [27; 30]. Moreover, the number of papers for different prediction variables is summarized in Figure 8. The majority of the reviewed papers used chemical water quality variables, such as DO, BOD, and COD as outputs [30] in the systems of the river, lake, and WWTP. Furthermore, attention was also directed towards physical variables like pH, WT, and biological variables such as Chl-a. The number of diverse forecast lengths is shown in Figure 9. The forecast length in this review refers to the length of time to predict in advance. For example, if researchers used the historical data of the previous three days to predict the values of the current day, then the forecast length would be 1 [157]. However, 107 papers did not provide details about the forecast length which cast ambiguity and doubts to researchers in parameter settings [31]. It seems ideal to utilize ANN models to capture short-term (length = 1) relationships, as the process was carried out 30 times in 44 papers which provide details about the forecast length, while only 10 papers consider long-term (length > 1) forecasting. As mentioned in the Introduction, this review not only includes more water quality parameters but also more extensive research scenarios compared with the previous reviews. On the whole, there are 23 types of water quality variables examined in this review. They are mainly physical, chemical, and biological variables. In the field of water quality prediction, relatively mature sensors include DO, WT, Chl-a, pH, EC, and NH\({}_{3}\)-N. There are different application scenarios among the investigated water quality variables. Table 3 summarizes the main application scenarios of various water quality variables. Researchers conducted more prediction studies on DO, WT, Chl-a, pH, EC, NH\({}_{3}\)-N, Tur, and S than other water quality variables. It can be seen from Table 3 that there are simple and practical  
Figure 8: Number of papers for different prediction variables. Figure 7: The distribution of papers between 2008 and 2019. Figure 9: The distribution of prediction lengths. sensors that can measure these water quality variables. Therefore, the extensive research of the above variables may benefit from the wide application of these sensors [148]. Table 4 summarizes the data set sizes of feedforward and recurrent neural networks involved in this review. According to Table 4, the number of samples applied for water quality prediction varies from 28 [39] to 45,594 [78] which illustrates the fact that ANN models are capable to deal with different size of the dataset. However, there has been no research studying the optimal amount of data required for each ANN model. As can be seen from Table 4, the recurrent neural networks [55] generally need more datasets compared with feedforward neural networks [139]. Research into the water quality parameter prediction have focused on rivers, WWTP, lake, and reservoir. In contrast, researchers have done little on artificial facilities, such as stream and pond. In the river system, most researchers use feed-forward neural networks for modeling, which may be due to the fact that the river system can be well analyzed using only the feed-forward neural network. This result also applies to WWTP systems.