Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Yiyang Wang, Dehao Xu, Xianpeng Li and Wei Wang ()
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Yiyang Wang: School of Electrical and Automation Engineering, Liaoning Institute of Science and Technology, Benxi 117004, China
Dehao Xu: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Xianpeng Li: College of Information Engineering, Dalian Ocean University, Dalian 116023, China
Wei Wang: College of Information Engineering, Dalian Ocean University, Dalian 116023, China

Mathematics, 2024, vol. 12, issue 5, 1-19

Abstract: The concentration of ammonia nitrogen is significant for intensive aquaculture, and if the concentration of ammonia nitrogen is too high, it will seriously affect the survival state of aquaculture. Therefore, prediction and control of the ammonia nitrogen concentration in advance is essential. This paper proposed a combined model based on X Adaptive Boosting (XAdaBoost) and the Long Short-Term Memory neural network (LSTM) to predict ammonia nitrogen concentration in mariculture. Firstly, the weight assignment strategy was improved, and the number of correction iterations was introduced to retard the shortcomings of data error accumulation caused by the AdaBoost basic algorithm. Then, the XAdaBoost algorithm generated and combined several LSTM su-models to predict the ammonia nitrogen concentration. Finally, there were two experiments conducted to verify the effectiveness of the proposed prediction model. In the ammonia nitrogen concentration prediction experiment, compared with the LSTM and other comparison models, the RMSE of the XAdaBoost–LSTM model was reduced by about 0.89–2.82%, the MAE was reduced by about 0.72–2.47%, and the MAPE was reduced by about 8.69–18.39%. In the model stability experiment, the RMSE, MAE, and MAPE of the XAdaBoost–LSTM model decreased by about 1–1.5%, 0.7–1.7%, and 7–14%. From these two experiments, the evaluation indexes of the XAdaBoost–LSTM model were superior to the comparison models, which proves that the model has good prediction accuracy and stability and lays a foundation for monitoring and regulating the change of ammonia nitrogen concentration in the future.

Keywords: aquaculture; adaptive boosting algorithm; LSTM; combined prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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