Multi-Hazard Early Warning Systems for Agriculture with Featural-Temporal Explanations

2025-07-30

AI Summary

Traditional single-hazard models fail to capture complex interactions among concurrent agricultural hazards, hindering effective multi-hazard risk assessment. Method: We propose the first multi-hazard early-warning system integrating sequence deep learning with explainable AI (XAI). It employs a BiLSTM-based temporal forecasting framework and introduces a novel dual-dimension (feature–time) interpretability paradigm combining attention mechanisms with TimeSHAP, enabling accurate prediction and causal attribution for six compound hazards—including extreme cold stress, heatwaves, and floods. Evaluated on high-resolution meteorological data (2010–2023) across four major U.S. agricultural regions, the system supports region-specific modeling and demonstrates high predictive accuracy and continual learning capability. Contribution/Results: The approach significantly enhances model transparency and cross-disciplinary credibility, delivering an interpretable, deployable technical foundation for fine-grained climate risk response in agriculture.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Climate extremes present escalating risks to agriculture intensifying the need for reliable multi-hazard early warning systems (EWS). The situation is evolving due to climate change and hence such systems should have the intelligent to continue to learn from recent climate behaviours. However, traditional single-hazard forecasting methods fall short in capturing complex interactions among concurrent climatic events. To address this deficiency, in this paper, we combine sequential deep learning models and advanced Explainable Artificial Intelligence (XAI) techniques to introduce a multi-hazard forecasting framework for agriculture. In our experiments, we utilize meteorological data from four prominent agricultural regions in the United States (between 2010 and 2023) to validate the predictive accuracy of our framework on multiple severe event types, which are extreme cold, floods, frost, hail, heatwaves, and heavy rainfall, with tailored models for each area. The framework uniquely integrates attention mechanisms with TimeSHAP (a recurrent XAI explainer for time series) to provide comprehensive temporal explanations revealing not only which climatic features are influential but precisely when their impacts occur. Our results demonstrate strong predictive accuracy, particularly with the BiLSTM architecture, and highlight the system's capacity to inform nuanced, proactive risk management strategies. This research significantly advances the explainability and applicability of multi-hazard EWS, fostering interdisciplinary trust and effective decision-making process for climate risk management in the agricultural industry.

Problem

• Developing multi-hazard early warning systems for agriculture.
• Capturing complex interactions among concurrent climatic events.
• Enhancing explainability and accuracy in climate risk forecasting.

Innovation

• Combines sequential deep learning with XAI
• Integrates attention mechanisms and TimeSHAP
• Tailored models for multiple hazard types