用机器学习预测短期干旱影响，提前八周预警农业与救灾损失。
Prediction and Forecast of Short-Term Drought Impacts Using Machine Learning to Support Mitigation and Adaptation Efforts
- 结合干旱指数与历史灾情数据，用XGBoost建模预测影响。
- 农业和救灾影响预测准确率最高，可提前八周预报。
- 为新墨西哥州生态干旱系统提供决策支持，适合干旱区应用。
干旱是一种复杂的自然灾害，常导致重大环境与经济损失。近年来干旱的严重性、频率和持续时间上升，凸显了有效监测与应对策略的必要性。仅预测干旱状况不足以支持主动决策，而预测干旱影响则有助于早期预警与主动响应。本研究利用机器学习方法，将干旱指数与2005至2024年的历史干旱影响记录（来自干旱影响报告器DIR）关联，实现短期影响预测。通过解决时间尺度与影响量化等关键挑战，提升在可行动时间窗口内的预测能力。采用干旱严重度与覆盖指数（DSCI）和蒸散胁迫指数（ESI），结合DIR数据，对每周干旱影响进行建模与预测。结果表明，火灾与救援影响预测精度最高，其次为农业与水资源，植物与社会类影响预测波动较大。基于新墨西哥州县与州级数据，使用梯度提升树模型（XGBoost）融合DSCI与ESI，成功实现了多数影响类别在八周内的提前预报。该工作支持了新墨西哥州生态干旱信息传播系统（EcoDri）的建设，并展示了在类似干旱易发区域的广泛应用潜力。研究成果可帮助利益相关方、土地管理者与决策者制定更有效的干旱减缓与适应策略。
原文摘要 · Abstract (English)
Drought is a complex natural hazard that affects ecological and human systems, often resulting in substantial environmental and economic losses. Recent increases in drought severity, frequency, and duration underscore the need for effective monitoring and mitigation strategies. Predicting drought impacts rather than drought conditions alone offers opportunities to support early warning systems and proactive decision-making. This study applies machine learning techniques to link drought indices with historical drought impact records (2005:2024) to generate short-term impact forecasts. By addressing key conceptual and data-driven challenges regarding temporal scale and impact quantification, the study aims to improve the predictability of drought impacts at actionable lead times. The Drought Severity and Coverage Index (DSCI) and the Evaporative Stress Index (ESI) were combined with impact data from the Drought Impact Reporter (DIR) to model and forecast weekly drought impacts. Results indicate that Fire and Relief impacts were predicted with the highest accuracy, followed by Agriculture and Water, while forecasts for Plants and Society impacts showed greater variability. County and state level forecasts for New Mexico were produced using an eXtreme Gradient Boosting (XGBoost) model that incorporated both DSCI and ESI. The model successfully generated forecasts up to eight weeks in advance using the preceding eight weeks of data for most impact categories. This work supports the development of an Ecological Drought Information Communication System (EcoDri) for New Mexico and demonstrates the potential for broader application in similar drought-prone regions. The findings can aid stakeholders, land managers, and decision-makers in developing and implementing more effective drought mitigation and adaptation strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。