Global debris flow susceptibility based on a comparative analysis of a single global model versus a continent-by-continent approach

Abstract

Debris flows, and landslides in general, are worldwide catastrophic phenomena. As world population and urbanization grow in magnitude and geographic coverage, the need exists to extend focus, research, and modeling to a continental and global scale. Although debris flow behavior and parameters are local phenomena, sound generalizations can be applied to debris flow susceptibility analyses at larger geographic extents based on these criteria. The focus of this research is to develop a global debris flow susceptibility map by modeling at both a continental scale for all continents and by a single global model and determine whether a global model adequately represents each continent. Probability Density, Conditional Probability, Certainty Factor, Frequency Ratio, and Maximum Entropy statistical models were developed and evaluated for best model performance using fourteen environmental factors generally accepted as the most appropriate debris flow predisposing factors. Global models and models for each continent were then developed and evaluated against verification data. The comparative analysis demonstrates that a single global model performs comparably or better than individual continental models for a majority of the continents, resulting in a debris flow susceptibility map of the world useful for in-depth research, international planning, and future debris flow susceptibility modeling and determining societal impacts.

Future global debris flow susceptibility considering climate change, wildfire probability, and glacier retreat

“…A Maximum Entropy "present-day" global debris flow susceptibility model developed by Kurilla and Fubelli 2022 [22], with MaxEnt software [23] was used for derivation of factors and factor classes associated with an inventory of 5695 historic debris flow events. The "present-day" Maximum Entropy model is used to extract those factors with significance values > 1% and their factor classes which exhibit >= 90% predicted probability, as suitable debris flow environments.…”

Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis

“…In terms of subject types, these 13 journals primarily cover earth science, remote sensing science, disaster science, hydrology science, geological science, and mountain science. This diversity underscores that the prediction of debris flow involves knowledge from many subject fields.In this study, the geographical distribution of the research areas and the first institutions of the 84 papers was determined, according to the country reported in the papers, with the exclusion of the two intercontinental-scale papers[56,57]. Out of the 84 papers, the research regions of the paper[58] corresponded to 8 countries, with the remaining papers each corresponding to a single country.…”