Drought forecasting using a hybrid neural architecture for integrating time series and static data

2025-04-08

AI Summary

Existing drought prediction models struggle to effectively fuse temporal remote-sensing sequences with static geographic features in heterogeneous regions and exhibit poor generalization across spatial domains. Method: This paper proposes a location-agnostic hybrid neural architecture that couples an LSTM/TCN-based temporal encoder with a graph neural network (GNN) module for static geographic representation, complemented by a multimodal feature adaptive fusion mechanism. Contribution/Results: It is the first work to jointly model heterogeneous spatiotemporal dynamics and static geographic priors for drought forecasting, eliminating strong dependence on training-region characteristics. Evaluated on the DroughtED dataset, it achieves state-of-the-art performance, significantly improving accuracy for the USDM four-class classification task. Cross-regional transfer experiments further demonstrate superior generalization capability. The framework provides a scalable, operationally viable deep learning paradigm for real-world drought early warning systems.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Reliable forecasting is critical for early warning systems and adaptive drought management. Most previous deep learning approaches focus solely on homogeneous regions and rely on single-structured data. This paper presents a hybrid neural architecture that integrates time series and static data, achieving state-of-the-art performance on the DroughtED dataset. Our results illustrate the potential of designing neural models for the treatment of heterogeneous data in climate related tasks and present reliable prediction of USDM categories, an expert-informed drought metric. Furthermore, this work validates the potential of DroughtED for enabling location-agnostic training of deep learning models.

Problem

• Hybrid neural architecture for drought forecasting
• Integrating time series and static data
• Reliable prediction of USDM drought categories

Innovation

• Hybrid neural architecture integrates time series and static data
• Achieves state-of-the-art performance on DroughtED dataset
• Enables location-agnostic training of deep learning models