## 4 Discussion
[37], TSS tended to accumulate in the intensive \(L\)_vanname_ culture and was eventually reflected in an increase in NTU. As both turbidity and TSS can reflect the clarity of a liquid, the turbidity parameter was used for modeling in this study. The principal component analysis (PCA) results for dimensionality reduction showed that turbidity, dissolved oxygen, pH, and temperature could be used as the leading indicators for modeling. The variable-flow regulation model obtains the current water quality indicators in real time and then applies these indicators to predict and classify the circulation rate for the next period. The turbidity sensor in turbulent flow had a measured data fluctuation that was too large, and the sensor arrangement position also caused measurement errors. An innovative point of this study is that the drum filter backwash frequency over a certain period was used as one of the critical factors for modeling instead of the momentary RAS water turbidity. Backwash times can effectively replace turbidity reading to reflect overall RAS water turbidity, avoiding the instability of the data collected by the turbidity sensor. The application of machine learning methods in aquaculture-related research is focused mainly on the prediction, classification, and evaluation of water quality indicators such as dissolved oxygen, salinity, pH, ammonia, and nitrite [25]. In the present study, machine learning was used to model the variable-flow regulation strategy. Sensors collected water indicators, including DO, pH, temperature, and turbidity. In order to implement the variable-flow principle, the machine learning methods were introduced in the present study to develop the optimal variable-flow regulation model for RAS. The water quality indicators, the backwash frequency, and the circulating pump frequency were obtained through continuous monitoring. For the ANN methods, the LSTM model was identified as the optimal regulation model, since the accuracy and F1-score indicators reflected the strong ability of the LSTM classifier. The modeling data based on time series were collected from the continuously running RAS in the present study. The water quality indicators, backwash frequency, and total circulation rates were recorded through the fixed time interval during the whole rearing period. Research has shown that LSTM can indeed perform well in processing long time series sequences of data [38]. The optimal classification model needs to be relatively simple in order to be applied in the embedded devices. The variable-flow adjustment strategy in RAS also needs to respond quickly and satisfy the high standard of classification accuracy. All the evaluated indicators of the SVM models demonstrated better results compared with the LSTM model. The gene algorithm contributed the highest accuracy and F1-score among the four optimization algorithms in the classification task. As a supervised algorithm, GA-SVM can be applied to effectively adjust water refreshment in RAS. In future work on variable-flow RAS regulation, the data-driven model needs to be improved to establish continuous variable-flow control technology by adjusting circulating pump frequency. A larger quantity of data from the running RAS can ensure higher availability and robustness for optimizing the intelligent variable-flow strategy. The continuous variable-flow control technology prerequisite is required for the indicators (water quality, backwash frequency, and rearing cycle) to correspond to the ideal circulation volume. Furthermore, the interaction effects between various indicators need to be revealed through experiments and analysis. The ultimate goal of the study is to achieve a precise circulation control strategy in the RAS and execute rapid water treatment without affecting the health of the reared animals.