A Multi-Source Early Warning System of MEMS Based Wireless Monitoring for Rainfall-Induced Landslides

• Published in: Applied sciences Vol. 7; no. 12; p. 1234
• Main Authors: Yang, Zongji, Shao, Wei, Qiao, Jianping, Huang, Dong, Tian, Hongling, Lei, Xiaoqin, Uchimura, Taro
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29.11.2017
• Subjects: American dollar; Case studies; cost-effectiveequipment; Early warning systems; Earthquake prediction; Earthquakes; Economic impact; Emergency communications systems; Extensometers; Global positioning systems; GPS; Ground motion; In situ measurement; Landslides; Landslides & mudslides; Matric suction; Mechanical systems; Microelectromechanical systems; Moisture content; Monitoring; Monitoring systems; Mountains; Multivariate analysis; multivariate thresholds; portable sensor unit; Precipitation; Rainfall; rainfall-induced landslides; Seismic activity; Sensors; Slope stability; Soil moisture; Soil suction; Stability analysis; State variable; Vibration; Warning systems; Water content; wireless early warning system; Wireless networks; United States > US; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app7121234

Landslide monitoring and early warning systems are the most successful countermeasures to reduce fatalities and economic losses from landslide hazards. The traditional strategies such as GPS and extensometers are relatively expensive and difficult to be installed in steep, high mountains. In this study, a MEMS (Micro Electro Mechanical Systems) based multivariate wireless monitoring sensor unit was used to build a monitoring and early warning system for rainfall-triggered landslides, as one of the most practical and cost-effective countermeasures for hard-reached mountains. The multi-source wireless monitoring system and its well-developed equipment were tested in a landslide-prone slope to monitor the triggering of landslides and debris flows in the Wenchuan earthquake region, China. The variations of several state variables were observed, including the soil moisture content, soil matric suction, rainfall, inclination and ground vibration. The results of a slope stability analysis were benchmarked with the in situ measurements to identify the multivariate early warning parameters for rainfall-induced landslides. The proposed early warning system for the slope stability analysis can provide a more accurate prediction for rainfall-induced landslides and debris flows in earthquake hit regions.