A study on the application and optimization of deep learning algorithms in soil erosion and secondary disaster early warning

This paper studies the application and optimization of deep learning algorithms in soil erosion and secondary disaster warning, aiming to improve the accuracy and practicality of disaster prediction. By constructing a CNN-LSTM hybrid model and combining multidimensional data such as rainfall, terrain characteristics and soil moisture, accurate prediction of soil erosion and secondary disaster risks is achieved. The study uses standardization and sliding window methods to preprocess the data, and optimizes the model performance through Dropout regularization, data enhancement and hyperparameter adjustment. The experimental results show that the accuracy of the optimized model is increased from 72% to 85%, and the MSE is reduced from 0.035 to 0.028, which significantly improves the prediction accuracy. Visual analysis such as ROC curves and three-dimensional risk surface maps further reveal the impact of environmental factors on disaster risks, providing a basis for setting warning thresholds. This study verifies the application potential of deep learning technology in disaster warning and provides effective technical support for related fields.