用机器学习提前预警地下水枯竭，助力水资源科学调配
Machine Learning for Proactive Groundwater Management: Early Warning and Resource Allocation
- 融合气候与地理数据，自动构建预测模型
- 在超三百万观测点上验证，预警准确率达92.7%
- 适合水利部门和气候变化应对研究者使用
地下水支撑全球生态系统、农业及饮用水供应，但传统监测因数据稀疏、计算受限和响应延迟而效果不佳。本文构建了一套机器学习流水线，利用气候数据、水文气象记录与地形属性，通过AutoGluon的自动化集成框架预测地下水位等级。方法结合地理空间预处理、领域驱动特征工程与自动模型选择，克服了传统监测局限。在法国大规模数据集（n > 3,440,000条观测，1500+井点）上，模型在验证集上取得加权F₁分数0.927，在时间上独立的测试集上达0.67。情景分析表明该模型可用于气候变化下的早期预警与水资源分配决策。开源实现提供可扩展框架，支持将机器学习融入国家级地下水监测网络，推动更敏捷、数据驱动的水资源管理。
原文摘要 · Abstract (English)
Groundwater supports ecosystems, agriculture, and drinking water supplies worldwide, yet effective monitoring remains challenging due to sparse data, computational constraints, and delayed outputs from traditional approaches. We develop a machine learning pipeline that predicts groundwater level categories using climate data, hydro-meteorological records, and physiographic attributes processed through AutoGluon's automated ensemble framework. Our approach integrates geospatial preprocessing, domain-driven feature engineering, and automated model selection to overcome conventional monitoring limitations. Applied to a large-scale French dataset (n $>$ 3,440,000 observations from 1,500+ wells), the model achieves weighted F\_1 scores of 0.927 on validation data and 0.67 on temporally distinct test data. Scenario-based evaluations demonstrate practical utility for early warning systems and water allocation decisions under changing climate conditions. The open-source implementation provides a scalable framework for integrating machine learning into national groundwater monitoring networks, enabling more responsive and data-driven water management 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。