论文标题
ML基于大篷车数据集的全球河流洪水预测框架
ML framework for global river flood predictions based on the Caravan dataset
论文作者
论文摘要
在开始的72小时内，可靠的河流洪水预测可以减少危害，因为紧急机构有足够的时间准备和部署在现场帮助。这种河流洪水预测模型已经存在，并且在大多数高收入国家中的表现相对良好。但是，由于数据的可用性有限，低收入国家缺乏这些模型。在这里，我们根据新出版的大篷车数据集提供了第一个全球河流洪水预测框架。我们的框架旨在作为未来全球河流洪水预测研究的基准。为了支持概括性主张，我们包括自定义数据评估拆分。此外，我们针对三个基线模型提出并评估了一种新型的两路LSTM体系结构（2P-LSTM）。最后，我们评估了非洲和亚洲不同地点的生成模型，这些模型不是大篷车数据集的一部分。
Reliable prediction of river floods in the first 72 hours can reduce harm because emergency agencies have sufficient time to prepare and deploy for help at the scene. Such river flood prediction models already exist and perform relatively well in most high-income countries. But, due to the limited availability of data, these models are lacking in low-income countries. Here, we offer the first global river flood prediction framework based on the newly published Caravan dataset. Our framework aims to serve as a benchmark for future global river flood prediction research. To support generalizability claims we include custom data evaluation splits. Further, we propose and evaluate a novel two-path LSTM architecture (2P-LSTM) against three baseline models. Finally, we evaluate the generated models on different locations in Africa and Asia that were not part of the Caravan dataset.
