# Landslide Susceptibility Modeling by Interpretable Neural Network

arXiv: https://arxiv.org/abs/2201.06837 · HTML: https://mlsanity.com/paper/2201.06837/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 112 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation](https://mlsanity.com/paper/1710.06169/) (2017)
- [Machine Learning Approach to RF Transmitter Identification](https://mlsanity.com/paper/1711.01559/) (2017)
- [Do optimization methods in deep learning applications matter?](https://mlsanity.com/paper/2002.12642/) (2020)
- [A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design](https://mlsanity.com/paper/1906.01246/) (2019)

## Built on by (most cited first, 3 of 3)

- [Landslide mapping from Sentinel-2 imagery through change detection](https://mlsanity.com/paper/2405.20161/) (2024)
- [Knowledge-infused Deep Learning Enables Interpretable Landslide Forecasting](https://mlsanity.com/paper/2307.08951/) (2023)
- [Synergising Local Geo-Environmental Characteristics with Spatial Context for Enhancing Landslide Susceptibility Mapping](https://mlsanity.com/paper/2608.24956/) (2026)

## Abstract (arXiv)

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 inventory 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.