Using Neural Networks to Label Rain Warning for Natural Hazard of Slope

The landslides and flows cause significant direct damage to lives and property. A system for monitoring these signs can be the most powerful tool for disaster prevention. In the natural hazard of slope, the signs for rain warning is very useful for disaster prevention. Labeling the rain warning seems to be an important and useful job for disaster prevention. In this paper, two neural network models are used for labeling the rain warning. These two models are the multilayer perceptron (MLP) and the long short-term memory (LSTM). The raw data consist of four observations such as time (time), rainfall (rain), groundwater level (W1) and displacements of inclinometers (SAA-11 and SAA-20). The RMSE (Root Mean Squared Error) using LSTM is 0.161 and RMSE using MLP is 0.212. In the experimental results, LSTM is better than MLP.