用卫星数据+无监督学习，发现加纳地下水异常变化规律
Unsupervised Detection of Groundwater Storage Anomalies in Ghana Using GRACE Satellite Data
- 结合GRACE卫星与孤立森林算法，无监督检测地下水异常
- 发现12个异常月，2018年后正异常增多，北部缺水更频繁
- 比传统阈值法更灵敏，适合缺乏观测数据的地区
加纳地下水变化因长期实地观测不足而难以准确刻画。本研究利用2004-2024年GRACE卫星数据，结合统计分析与无监督机器学习方法，对地下水储存异常进行检测。通过Z-score标准化异常值，采用基于集成的孤立森林框架识别异常。结果表明，2004-2009年持续存在地下水亏缺，2018年后正异常显著上升。共识别出12个异常月，其中5个月为亏缺、7个月为盈余，最强异常对应地下水亏缺。空间分析显示，北部地区缺水异常更频繁，南部则出现更强的盈余。与统计阈值对比表明，该机器学习方法捕捉到更多细微偏差，超越传统方法。整体而言，将GRACE观测与无监督异常检测结合，为数据匮乏地区的地下水监测提供了可行框架。
原文摘要 · Abstract (English)
Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations. This study investigates groundwater storage anomalies using GRACE-derived data from 2004-2024 combined with statistical analysis and unsupervised machine learning. Groundwater anomalies were standardized using Z-scores, while an ensemble-based Isolation Forest framework was applied for anomaly detection. The results revealed substantial temporal variability, with persistent groundwater deficits during 2004-2009 followed by increasing positive anomalies after 2018. A total of 12 anomalous months were identified, comprising 5 deficit and 7 surplus events, with the strongest anomalies associated with groundwater deficits. Spatial analysis showed more frequent deficit anomalies in northern Ghana and stronger surplus occurrence in southern regions. Comparison with statistical thresholds further indicated that the machine learning framework captured additional subtle deviations beyond conventional threshold-based methods. Overall, the integration of GRACE observations with unsupervised anomaly detection provides a practical framework for groundwater monitoring in data-scarce environments.
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