Modern aquaculture requires faster and more accurate water quality prediction,so improving the accuracy of aquaculture water quality prediction is of great significance for improving the efficiency of aquaculture.A fishery water quality prediction model combining random forest,SG filter,Informer,and LSTM variants is proposed for this purpose.Firstly,fill in missing values in the data using the random forest algorithm,and then reduce noise interference through the SG filter.Combining LSTM with static real-time recursive network as the internal structure of Informer,data is fed into the model,and finally water quality prediction results are obtained through fully connected layer output.Predict the water temperature,pH value,and dissolved oxygen at monitoring points within the fishing ground.The results indicate that the proposed method improves the prediction accuracy of water quality in aquaculture.
water quality predictiontime serieslong short-term memoryInformer
NETL
NSTL
万方数据
