KEYWORDS: Data modeling, Neurons, Computer simulations, Education and training, Data fusion, Wind speed, Data acquisition, Mathematical optimization, Overfitting, Artificial intelligence
To address the issue of low accuracy in simulating the motion trajectory of water-floating garbage due to multiple factors, a method for simulating the drift trajectory of water-floating garbage based on Sa-LSTM was proposed. The method taking the drift trajectory of water-floating garbage in Lanzhou Section of the Yellow River as the research object, integrated multiple influencing factors through feature derivation and enhanced the memory and generalization ability of LSTM model by using spatial attention module, which further improved the accuracy of water-floating garbage simulation data. The experimental results show that the proposed method can effectively reduce the interference of multiple influencing factors on the simulation of water-floating garbage drifting trajectory, improve the accuracy of drifting trajectory simulation, and provide a method and location information support for the accurate management and management of water-floating garbage.
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