An Improved Data-Driven Approach for the Prediction of Rainfall-Triggered Soil Slides Using Downscaled Remotely Sensed Soil Moisture

Abstract

The infiltration of rainwater into soil slopes leads to an increase of porewater pressure and destruction of matric suction, which causes a reduction in soil shear strength and slope instability. Hence, surface moisture and infiltration properties must be direct inputs in reliable landslide hazard assessment methods. Since the in situ measurement of pore pressure is expensive, the use of remotely sensed soil moisture is practically feasible. Downscaling improves the spatial resolution of soil moisture for a better representation of specific local conditions. Downscaled soil moisture, the relevant geotechnical properties of saturated hydraulic conductivity and soil type, and the conditioning factors of elevation, slope, and distance to roads are used to develop an improved logistic regression model to predict the soil slide hazard of soil slopes using data from two geographically different regions. A soil moisture downscaling model with a better accuracy than the downscaling models that have been used in previous landslide studies is employed in this study. This model provides a good classification accuracy and performs better than the alternative water drainage-based indices that are conventionally used to quantify the effect that elevated soil moisture has upon the soil slide hazard. Furthermore, the downscaling of soil moisture content is shown to improve the prediction accuracy. Finally, a technique that can provide the threshold probability for identifying locations with a high soil slide hazard is proposed.