Guidance Index for Shallow Landslide Hazard Analysis
AbstractRainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity and/or duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. As of this day, no system exists that dynamically interrelates these two factors on large scales. This work introduces a Shallow Landslide Index (SLI) as the first implementation of antecedent soil moisture conditions for the hazard analysis of shallow rainfall-induced landslides. The proposed mathematical algorithm is built using a logistic regression method that systematically learns from a comprehensive landslide inventory. Initially, root-soil moisture and rainfall measurements modeled from AMSR-E and TRMM respectively, are used as proxies to develop the index. The input dataset is randomly divided into training and verification sets using the Hold-Out method. Validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. Consecutively, as AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, root-soil moisture and rainfall measurements modeled by SMAP and GPM are used to develop models that calculate the SLI for 10, 7, and 3 days. The resulting models indicate a strong relationship (78.7%, 79.6%, and 76.8% respectively) between the predictors and the predicted value. The results also highlight important remaining challenges such as adequate information for algorithm functionality and satellite based data reliability. Nevertheless, the experimental system can potentially be used as a dynamic indicator of the total amount of antecedent moisture and rainfall (for a given duration of time) needed to trigger a shallow landslide in a susceptible area. It is indicated that the SLI algorithm can be re-built for other regions where deterministic studies are not feasible. This represents a significant step towards rainfall-induced shallow landslide hazard readiness. View Full-Text
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Avalon Cullen, C.; Al-Suhili, R.; Khanbilvardi, R. Guidance Index for Shallow Landslide Hazard Analysis. Remote Sens. 2016, 8, 866.
Avalon Cullen C, Al-Suhili R, Khanbilvardi R. Guidance Index for Shallow Landslide Hazard Analysis. Remote Sensing. 2016; 8(10):866.Chicago/Turabian Style
Avalon Cullen, Cheila; Al-Suhili, Rafea; Khanbilvardi, Reza. 2016. "Guidance Index for Shallow Landslide Hazard Analysis." Remote Sens. 8, no. 10: 866.
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