Artificial neural network intelligent system on the early warning system of landslide

Sofwan Aghus, . and Sumardi, . and Najib, . and Bhirawa Indrah Wendah Atma, . Artificial neural network intelligent system on the early warning system of landslide. pp. 943-958. ISSN 2231-8526

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 grounds 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.

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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 grounds 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.

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Item Type:	Article
AGROVOC Term:	Landslides
AGROVOC Term:	Natural disasters
AGROVOC Term:	Computer hardware
AGROVOC Term:	Data collection
AGROVOC Term:	Sampling
AGROVOC Term:	Early warning systems
AGROVOC Term:	Computer programming
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/9789

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