Version 1
: Received: 29 October 2024 / Approved: 29 October 2024 / Online: 29 October 2024 (11:18:17 CET)
How to cite:
Hidayat, M.; Hazarika, H.; Kanaya, H. Calibration and Performance Evaluation of Cost-effective Capacitive Moisture Sensor in a Slope Model Experiments. Preprints2024, 2024102280. https://doi.org/10.20944/preprints202410.2280.v1
Hidayat, M.; Hazarika, H.; Kanaya, H. Calibration and Performance Evaluation of Cost-effective Capacitive Moisture Sensor in a Slope Model Experiments. Preprints 2024, 2024102280. https://doi.org/10.20944/preprints202410.2280.v1
Hidayat, M.; Hazarika, H.; Kanaya, H. Calibration and Performance Evaluation of Cost-effective Capacitive Moisture Sensor in a Slope Model Experiments. Preprints2024, 2024102280. https://doi.org/10.20944/preprints202410.2280.v1
APA Style
Hidayat, M., Hazarika, H., & Kanaya, H. (2024). Calibration and Performance Evaluation of Cost-effective Capacitive Moisture Sensor in a Slope Model Experiments. Preprints. https://doi.org/10.20944/preprints202410.2280.v1
Chicago/Turabian Style
Hidayat, M., Hemanta Hazarika and Haruichi Kanaya. 2024 "Calibration and Performance Evaluation of Cost-effective Capacitive Moisture Sensor in a Slope Model Experiments" Preprints. https://doi.org/10.20944/preprints202410.2280.v1
Abstract
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 po-tential to be widely implemented across multiple sites for landslide early warning systems. How-ever, 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 re-search 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 determi-nation (R²) ranging from 0.92 to 0.98 and a root mean square error (RMSE) ranging from 1.17 to 2.67. The calibration equation was validated through slope model experiments, with soil samples taken from the model after the experiment finished. Overall, the moisture content readings from the sensors showed approximately a 10% deviation from the actual moisture content. The findings suggest that the cost-effective capacitive soil moisture sensors has the potential to be used for the development of landslide early warning system
Keywords
soil moisture sensor; water content; sensor calibration; slope model; internet of things
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
