Artificial Neural Network Intelligent System on the Early Warning System of Landslide

Abstract

Landslide is a natural sloping ground movement disaster that can occur due to several factors such as high rainfall, soil moisture in the depth of the soil of an area, vibrations experienced in the region, and the slope of the ground structure. A system that can deliver these factor values into the levels of vulnerability of landslide disasters is needed. The system uses Arduino Mega 2560 to process the level of vulnerability. It can predict the moment and the probability of the disaster occurring as an early warning system. The artificial neural network (ANN) intelligent system can expect an event of a disaster. The designed ANN used five parameters causing landslide as input data: rainfall, slope, soil moisture on the surface, soil moisture in the ground’s depth, and soil vibration. The ANN system output delivered three-level conditions: the safe, the standby, and the hazardous. The feed-forward backpropagation (FFBP) and the cascade forward backpropagation (CFBP) methods were analyzed. The performance of both methods was compared in terms of minimum square error (MSE). The MSE results of FFBP and CFBP in the safe, the standby, and the hazardous conditions were 0.017076 and 0.034952; 0.049597 and 0.046764; 0.062105 and 0.060355; respectively. The results point to the supremacy of CFBP to FFBP in standby and hazardous conditions. Therefore, the CFBP is implemented into the hardware of the early warning system.