Hidayat Muhammad Nurjati, Hazarika Hemanta, Kanaya Haruichi
Graduate School of Engineering, Kyushu University, Fukuoka 819-0395, Japan.
Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan.
Sensors (Basel). 2024 Dec 20;24(24):8156. doi: 10.3390/s24248156.
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.
了解导致边坡失稳的因素，如土壤饱和度，对于减轻降雨引发的山体滑坡至关重要。具有成本效益的电容式土壤湿度传感器有潜力在多个地点广泛应用于滑坡预警系统。然而，这些传感器需要针对特定应用进行校准，以确保读数的高精度。在本研究中，在实验室环境中进行了针对特定土壤的校准，以便将土壤湿度传感器与使用物联网（IoT）的自动监测系统集成。本研究旨在评估一种低成本土壤湿度传感器（SKU:SEN0193），并开发校准方程，用于在人工降雨条件下使用硅砂进行边坡模型实验。结果表明，多项式函数拟合效果最佳，决定系数（R）范围为0.918至0.983，均方根误差（RMSE）范围为1.171至2.488。校准方程通过边坡模型实验进行了验证，实验结束后从模型中采集了土壤样本。总体而言，传感器的湿度含量读数与实际湿度含量显示出约12%的偏差。研究结果表明，具有成本效益的电容式土壤湿度传感器有潜力用于滑坡预警系统的开发。
