Application of biomechanics and deep learning models in water quality monitoring
Abstract
This paper reviews the application of biomechanics and deep learning models in water quality monitoring, highlighting their potential to enhance the accuracy and efficiency of environmental pollution detection and prediction. Traditional water quality monitoring methods are difficult to deal with nonlinear and dynamic pollution data. This article reviews the fusion application of biomechanical models and deep learning (such as convolutional neural network (CNN), long short-term memory (LSTM)), and proves that it significantly improves monitoring accuracy (an average of 20% in cases) by simulating pollutant diffusion mechanisms (biomechanics) and mining complex data patterns (deep learning). In the future, it is necessary to establish an interdisciplinary collaboration framework to promote the deployment of lightweight models in real-time systems.
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