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interpretable-transformers-for-drought-forecasting-

The study developed a drought forecasting model for diverse climate zones in India. It proposed interpretable transformer for national-scale drought forecasts.

https://github.com/pathania-ashish/interpretable-transformers-for-drought-forecasting-

Science Score: 67.0%

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The study developed a drought forecasting model for diverse climate zones in India. It proposed interpretable transformer for national-scale drought forecasts.

Basic Info
  • Host: GitHub
  • Owner: pathania-ashish
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 18.9 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

InterpretableTransformersfordroughtforecasting

DOI

The study developed a drought forecasting model for diverse climate zones in India. It proposed interpretable transformer for national-scale drought forecasts. Abstract The impacts of climate change are increasingly visible in the form of severe droughts across all regions of the world, leading to amplified socio-economic and environmental repercussions. Proactive drought management requires effective forecasting and an improved understanding of the underlying hydro-climatic variables. The present study focuses on developing a national-scale drought forecasting model tailored to the diverse climatic zones of India. This model leverages the attention-based transformer framework to forecast SPEI-3 values at a lead time of 30, 60, and 90 days respectively while interpreting the complex spatiotemporal dependencies. The model predicted the SPEI-3 values with Root Means Square Error (RMSE) 0.66±0.08 and Nash-Sutcliffe Efficiency coefficient (NSE) 0.51±0.14 at a lead time of 30 days. Prediction uncertainty through quantile forecasting enhances the model's utility for effective decision-making and risk management. Model performance varies on the seasonal scale with higher accuracy in post-monsoon (Oct-Nov) and a relative decline in the pre-monsoon (March- May) season. Picture 1 Picture 12

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  • Login: pathania-ashish
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Pathania"
  given-names: "Ashish"
  orcid: "https://orcid.org/0009-0003-0675-0603"
title: "Interpretable Transformers for Drought forecasting"
version: 1.0
doi: 10.5281/zenodo.14168657
date-released: 2024-11-15
url: "https://doi.org/10.5281/zenodo.14168657"

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