Calibration and Performance Evaluation of Cost-Effective Capacitive Moisture Sensor in Slope Model Experiments.

Understanding the factors that contribute to slope failures, such as soil saturation, is essential for mitigating rainfall-induced landslides. Cost-effective capacitive soil moisture sensors have the potential to be widely implemented across multiple sites for landslide early warning systems. However, these sensors need to be calibrated for specific applications to ensure high accuracy in readings. In this study, a soil-specific calibration was performed in a laboratory setting to integrate the soil moisture sensor with an automatic monitoring system using the Internet of Things (IoT). This research aims to evaluate a low-cost soil moisture sensor (SKU:SEN0193) and develop calibration equations for the purpose of slope model experiment under artificial rainfall condition using silica sand. The results indicate that a polynomial function is the best fit, with a coefficient of determination (R) ranging from 0.918 to 0.983 and a root mean square error (RMSE) ranging from 1.171 to 2.488. The calibration equation was validated through slope model experiments, with soil samples taken from the models after the experiment finished. Overall, the moisture content readings from the sensors showed approximately a 12% deviation from the actual moisture content. The findings suggest that the cost-effective capacitive soil moisture sensor has the potential to be used for the development of landslide early warning system.