Yeditha Pavan Kumar, Kasi Venkatesh, Rathinasamy Maheswaran, Agarwal Ankit
Department of Civil Engineering, MVGR College of Engineering, Vijayanagaram 535005, India.
Department of Hydrology, Indian Institute of Technology, Roorkee 247667, India.
Chaos. 2020 Jun;30(6):063115. doi: 10.1063/5.0008195.
An accurate and timely forecast of extreme events can mitigate negative impacts and enhance preparedness. Real-time forecasting of extreme flood events with longer lead times is difficult for regions with sparse rain gauges, and in such situations, satellite precipitation could be a better alternative. Machine learning methods have shown promising results for flood forecasting with minimum variables indicating the underlying nonlinear complex hydrologic system. Integration of machine learning methods in extreme event forecasting motivates us to develop reliable flood forecasting models that are simple, accurate, and applicable in data scare regions. In this study, we develop a forecasting method using the satellite precipitation product and wavelet-based machine learning models. We test the proposed approach in the flood-prone Vamsadhara river basin, India. The validation results show that the proposed method is promising and has the potential to forecast extreme flood events with longer lead times in comparison with the other benchmark models.
准确及时地预测极端事件可以减轻负面影响并加强防范。对于雨量计稀少的地区，提前较长时间对极端洪水事件进行实时预测很困难，在这种情况下，卫星降水可能是更好的选择。机器学习方法在利用最少变量进行洪水预测方面已显示出有前景的结果，这些变量表明了潜在的非线性复杂水文系统。将机器学习方法整合到极端事件预测中促使我们开发可靠的洪水预测模型，这些模型简单、准确且适用于数据匮乏地区。在本研究中，我们利用卫星降水产品和基于小波的机器学习模型开发了一种预测方法。我们在印度洪水多发的瓦姆萨德哈拉河流域对所提出的方法进行了测试。验证结果表明，与其他基准模型相比，所提出的方法很有前景，有潜力提前较长时间预测极端洪水事件。
