# IceWatch: Forecasting Glacial Lake Outburst Floods (GLOFs) using Multimodal Deep Learning

arXiv: https://arxiv.org/abs/2601.12330 · HTML: https://mlsanity.com/paper/2601.12330/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks](https://mlsanity.com/paper/2009.00191/) (2020)
- [Glacier Calving Front Segmentation Using Attention U-Net](https://mlsanity.com/paper/2101.03247/) (2021)
- [GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping](https://mlsanity.com/paper/2204.05818/) (2022)
- [Multi-Sensor Deep Learning for Glacier Mapping](https://mlsanity.com/paper/2409.12034/) (2024)
- [Predicting ice flow using machine learning](https://mlsanity.com/paper/1910.08922/) (2019)

## Abstract (arXiv)

Glacial Lake Outburst Floods (GLOFs) pose a serious threat in high mountain regions. They are hazardous to communities, infrastructure, and ecosystems further downstream. The classical methods of GLOF detection and prediction have so far mainly relied on hydrological modeling, threshold-based lake monitoring, and manual satellite image analysis. These approaches suffer from several drawbacks: slow updates, reliance on manual labor, and losses in accuracy when clouds interfere and/or lack on-site data. To tackle these challenges, we present IceWatch: a novel deep learning framework for GLOF prediction that incorporates both spatial and temporal perspectives. The vision component, RiskFlow, of IceWatch deals with Sentinel-2 multispectral satellite imagery using a CNN-based classifier and predicts GLOF events based on the spatial patterns of snow, ice, and meltwater. Its tabular counterpart confirms this prediction by considering physical dynamics. TerraFlow models glacier velocity from NASA ITS_LIVE time series while TempFlow forecasts near-surface temperature from MODIS LST records; both are trained on long-term observational archives and integrated via harmonized preprocessing and synchronization to enable multimodal, physics-informed GLOF prediction. Both together provide cross-validation, which will improve the reliability and interpretability of GLOF detection. This system ensures strong predictive performance, rapid data processing for real-time use, and robustness to noise and missing information. IceWatch paves the way for automatic, scalable GLOF warning systems. It also holds potential for integration with diverse sensor inputs and global glacier monitoring activities.