An Analytical Study on Soil Water Index (SWI), Landslide Prediction and Other Related Factors Using XRAIN Data during the July 2018 Heavy Rain Disasters in Hiroshima, Japan

Abstract

The rainfall-induced landslide disasters in July 2018 in Southwestern Japan yet again exemplified the severity of slope failure-related damage and the need for improvement of early warning systems. The Japanese Meteorological Agency (JMA) uses a method based on a threshold value of soil water index (SWI), a conceptual measurement that represents saturation of slope soil. The current SWI early warning system uses 60-min rainfall data on a 5-km2 mesh and does not take into consideration other landslide conditioning factors such as slope angle and geology. This study calculates SWI values during the July 2018 disasters in Kure City (Hiroshima Prefecture) using 1-min XRAIN rainfall data in a 250-m mesh to investigate the relationship between SWI and landslide occurrence. It was found that the SWI threshold of 124 mm used in the JMA early warning system for the area was surpassed in all cells. A new SWI threshold calculation method taking slope angle and geology into consideration and produced with machine learning is proposed, comprising power lines for different geological units at a two-dimensional graph where points located above the threshold line represent landslide risk. It is judged that this method would provide a more accurate early warning system for landslide disasters.