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Title: Landslide susceptibility modeling by interpretable neural network
Abstract Landslides are notoriously difficult to predict because numerous spatially and temporally varying factors contribute to slope stability. Artificial neural networks (ANN) have been shown to improve prediction accuracy but are largely uninterpretable. Here we introduce an additive ANN optimization framework to assess landslide susceptibility, as well as dataset division and outcome interpretation techniques. We refer to our approach, which features full interpretability, high accuracy, high generalizability and low model complexity, as superposable neural network (SNN) optimization. We validate our approach by training models on landslide inventories from three different easternmost Himalaya regions. Our SNN outperformed physically-based and statistical models and achieved similar performance to state-of-the-art deep neural networks. The SNN models found the product of slope and precipitation and hillslope aspect to be important primary contributors to high landslide susceptibility, which highlights the importance of strong slope-climate couplings, along with microclimates, on landslide occurrences.  more » « less
Award ID(s):
1945431
PAR ID:
10476118
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Communications earth & environment
Date Published:
Journal Name:
Communications Earth & Environment
Volume:
4
Issue:
1
ISSN:
2662-4435
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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