Development of Sacrificed Sensors for Rainfall-Triggered Shallow Landslide Monitoring

To assess the mechanism and have alert of rainfall-triggered shallow landslides, the main projectaims to propose an early-warning system accounting for rainfall events in practice. The main projectfirstly involves understanding of shallow landslide events using Big Data methodology (Sub-project 1).Rainfall-triggered shallow landslide simulation is then developed based on high resolution physicalbased model (Sub-project 2) and local area rainfall forecasting (Sub-project 5). Related landslide nondestructiveinspection (Sub-project 3) and field monitoring with sacrificed sensing system (Sub-project4) would support the characterization of landslides. The ultimate goal of main project is to reduce theimpact due to rainfall-triggered shallow landslides.Although some specific sensors and systems for debris flow monitoring have been developed and revealedbased on the OGC (Open Geospatial Consortium) and WSN (Wireless Sensor Network) protocols in Taiwan,on-site applications on rainfall-triggered shallow landslides need further verifications. A new generation opensourcebased system, such as Arduino, is famous for its cost-effective and easy-manipulated. Arduino is alsocapable to integrate numerous MEMS sensors as a WSN node. Furthermore, a single frequency GlobalPositioning System (GPS) chip is cost-down and may have enough resolution to record displacements oflandslides. A Time Domain Reflectometry (TDR) device, which combined with Low Power Wide AreaNetwork, such as LoRa WAN, is probably obtained for multi-node soil water content profiling in near future.Therefore, this study would like to examine these sensor and integrate the single frequency GPS via Arduinoplatform, as well as TDR penetrometer on single board. Measurements through a physical modeling and afield testing, combining well know Particle Tracking Velocimetry (PTV) or Digital Image Correlation (DIC)method, will be proceeded for capability verification. Sacrificed sensors and systems are expected to beemployed for rainfall-triggered shallow landslide monitoring in field. Corresponding results would supportthe decision making collaborated with the prediction from the non-linear, time series, or machine learningmethod.