Usage of normalized soil moisture for improving the performance of rainfall thresholds along transportation corridors

Landslides along transportation corridors pose substantial risks to infrastructure and public safety, necessitating accurate prediction and mitigation strategies. Many early warning systems for landslides are based on rainfall thresholds derived from historical data that distinguish landslide triggering from non-triggering events. However, it is widely recognized that antecedent moisture conditions have a major impact on the likelihood that a rainfall event will trigger a landslide. We aim to improve existing rainfall thresholds for landslides along highways by incorporating antecedent soil moisture conditions. The landslide inventory for this study was compiled from inclinometer data at suspected landslide sites and from landslide reports following major storm events along Alabama highways. This inventory was combined with precipitation data from the National Oceanic and Atmospheric Administration (NOAA) and soil moisture data from NASA's Soil Moisture Active Passive (SMAP) satellite. We evaluated the accuracy of rainfall thresholds from previous studies for forecasting landslides along highways in Alabama. Additionally, we investigated the potential of reducing the number of non-landslide events that exceed the thresholds (false positives) by utilizing soil moisture data derived from SMAP. This study demonstrates that sites with multiple inclinometers in a landslide region produce more robust datasets compared to those with a single inclinometer, enabling more effective differentiation between landslide and non-landslide events. Furthermore, using normalized soil moisture in the development of rainfall thresholds shows potential for reducing false positives, as approximately 75 % of the false positive cases in this study occurred when the soil moisture was at or below average conditions. Our proposed normalized soil moisture-dependent threshold framework will support decision-making systems by enabling users to weigh the trade-offs between potential false alarms and missed alarms, based on the relative costs or risks of each for a given project. The findings will aid transportation authorities and civil engineers in making informed decisions about potential interventions or preventive maintenance.