2024
1.
Krishu K. Thapa; Bhupinderjeet Singh; Supriya Savalkar; Alan Fern; Kirti Rajagopalan; Ananth Kalyanaraman
Attention-based Models for Snow-Water Equivalent Prediction Proceedings Article
In: Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24), 2024, (arXiv:2311.03388 [physics]).
Abstract | Links | BibTeX | Tags: AI, Snow Water Equivalent, Water
@inproceedings{thapa_attention-based_2023,
title = {Attention-based Models for Snow-Water Equivalent Prediction},
author = {Krishu K. Thapa and Bhupinderjeet Singh and Supriya Savalkar and Alan Fern and Kirti Rajagopalan and Ananth Kalyanaraman},
url = {http://arxiv.org/abs/2311.03388},
doi = {10.48550/arXiv.2311.03388},
year = {2024},
date = {2024-02-20},
urldate = {2024-02-20},
publisher = {Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24)},
abstract = {Snow Water-Equivalent (SWE) \textendash the amount of water available if snowpack is melted \textendash is a key decision variable used by water management agencies to make irrigation, flood control, power generation and drought management decisions. SWE values vary spatiotemporally \textendash affected by weather, topography and other environmental factors. While daily SWE can be measured by Snow Telemetry (SNOTEL) stations with requisite instrumentation, such stations are spatially sparse requiring interpolation techniques to create spatiotemporally complete data. While recent efforts have explored machine learning (ML) for SWE prediction, a number of recent ML advances have yet to be considered. The main contribution of this paper is to explore one such ML advance, attention mechanisms, for SWE prediction. Our hypothesis is that attention has a unique ability to capture and exploit correlations that may exist across locations or the temporal spectrum (or both). We present a generic attention-based modeling framework for SWE prediction and adapt it to capture spatial attention and temporal attention. Our experimental results on 323 SNOTEL stations in the Western U.S. demonstrate that our attention-based models outperform other machine learning approaches. We also provide key results highlighting the differences between spatial and temporal attention in this context and a roadmap toward deployment for generating spatially-complete SWE maps.},
note = {arXiv:2311.03388 [physics]},
keywords = {AI, Snow Water Equivalent, Water},
pubstate = {published},
tppubtype = {inproceedings}
}
Snow Water-Equivalent (SWE) – the amount of water available if snowpack is melted – is a key decision variable used by water management agencies to make irrigation, flood control, power generation and drought management decisions. SWE values vary spatiotemporally – affected by weather, topography and other environmental factors. While daily SWE can be measured by Snow Telemetry (SNOTEL) stations with requisite instrumentation, such stations are spatially sparse requiring interpolation techniques to create spatiotemporally complete data. While recent efforts have explored machine learning (ML) for SWE prediction, a number of recent ML advances have yet to be considered. The main contribution of this paper is to explore one such ML advance, attention mechanisms, for SWE prediction. Our hypothesis is that attention has a unique ability to capture and exploit correlations that may exist across locations or the temporal spectrum (or both). We present a generic attention-based modeling framework for SWE prediction and adapt it to capture spatial attention and temporal attention. Our experimental results on 323 SNOTEL stations in the Western U.S. demonstrate that our attention-based models outperform other machine learning approaches. We also provide key results highlighting the differences between spatial and temporal attention in this context and a roadmap toward deployment for generating spatially-complete SWE maps.
