Towards flexible groundwater-level prediction for adaptive water management: using Facebook’s Prophet forecasting approach

There is an increasing need for accurate groundwater-level (GWL) prediction to support effective seasonal water management. It is desirable for forecasting tools to be not only accurate but also accessible for decision-makers. We test the Prophet forecasting procedure, an open-source code released by Facebook, to address these challenges. It is based on an additive model considering non-periodic changes and periodic components in a Bayesian framework with easily-interpretable parameters. Predictions of daily GWL data in an area affected by pumping near a tourist complex in the Ramsar wetland area of Doñana (Spain) are compared to other forecasting methods. Prophet outperforms most methods in predicting GWL making it a fast and flexible forecasting tool for hydrologists and water managers. Furthermore, it allows gaining insight into the influence of each component of the forecast separately, helping to assess the hydrodynamic response to external drivers such as groundwater pumping.

精准的地下水位（groundwater-level, GWL）预测对于支撑高效的季节性水资源管理而言，需求日益增长。理想的预测工具不仅应具备高精度，还需便于决策者获取与使用。为应对上述挑战，我们测试了由Facebook发布的开源Prophet预测流程。该方法基于加性模型，在贝叶斯框架下考量非周期性变化与周期性分量，且参数具备良好可解释性。我们将西班牙多尼亚纳拉姆萨尔湿地内一处旅游综合体附近受抽水影响区域的日度GWL数据预测结果，与其他预测方法进行了对比。在GWL预测任务中，Prophet的表现优于多数方法，是水文研究者与水资源管理者可用的快速且灵活的预测工具。此外，该工具可单独拆解分析预测各分量的影响，助力评估地下水抽水等外部驱动因素下的水动力响应。