Research on water quality dissolved oxygen prediction method based on ESSA-LSTM for aquaculture ships
In order to accurately predict the dissolved oxygen content in water quality parameters,we adopted a Long Short Term Memory(LSTM)model,and proposed an Enhanced Sparrow Search Algorithm(ESSA)to improve the accuracy of the prediction rate.Besides,to prompt individual sparrows to swiftly depart from the local optimal solution,the algorithm intro-duced Circle chaotic mapping for population initialization,and integrated sine-cosine algorithm and Levy flight strategy to up-date the positions of scouts and trackers,respectively.Firstly,we compared ESSA with various other algorithms for multi form benchmark function testing,and the results reveal that the algorithm exhibited excellent performance and robustness on mul-tiple benchmark functions.Subsequently,we used ESSA to explore LSTM model parameters and compared it with other optimi-zation strategies,and the results show that the prediction rate based on ESSA-LSTM model reached 99.071％,which was im-proved by 2.142％,6.653％,6.682％and 7.714％compared with basic Sparrow Search Algorithm(SSA),Gray Wolf Optimiza-tion Algorithm(GWO),Marine Predation Algorithm(MPA),and Whale Optimization Algorithm(WOA),respectively.The re-sults show that the use of ESSA significantly improves the prediction rate of dissolved oxygen(DO)and effectively reduces the blindness and time cost of parameter settings.
Aquaculture shipsWater quality parametersLong Short Term Memory(LSTM)Sparrow search algorithmDis-solved oxygen prediction
万方数据
维普
