In order to improve the prediction accuracy of dissolved oxygen concentration(DOC)parameters in Taihu Lake,a prediction model based on ARIMA-IPOA-CNN-LSTM was designed.Firstly,the auto regressive integrated moving average(ARIMA)model was used to capture the time series trends and seasonal characteristics of the data.Sec-ondly,convolutional neural networks(CNN)and long short-term memory networks(LSTM)models were introduced to learn spatial and temporal features from the data,respectively.An improved pelican optimization algorithm(IPOA)was proposed to optimize model parameters.The proposed algorithm added methods such as Logistic chaotic mapping popula-tion initialization,reverse differential evolution,and firefly disturbance.The test results of the CEC2005 function were significantly better than those of traditional pelican optimization algorithms.Finally,the"pruning"model was deployed on the STM32 embedded device.Experimental results show that the model has high accuracy and robustness in predicting dissolved oxygen concentration,providing an efficient and reliable solution for water environment protection.
维普
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