Data accompanying the publication: Predicting Flood Inundation After a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network

This dataset contains all necessary data to produce the output presented in the paper "Predicting Flood Inundation After a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network", by L.S. Besseling, A. Bomers and S.J.M.H. Hulscher, published in Hydrology (2024). Included are the code for creating the LSTM neural network, the dataset from a 1D2D hydrodynamic HEC-RAS model on which the network was trained and tested, and helper files for running the code and visualizing results. A more detailed description of the dataset is provided in the Readme. For any further questions on the data, please contact the authors.

本数据集涵盖L.S. Besseling、A. Bomers与S.J.M.H. Hulscher于2024年发表于《Hydrology》期刊的论文《基于长短期记忆（Long Short-Term Memory, LSTM）神经网络预测堤坝决口后的洪水淹没范围》中展示的输出结果所需的全部必要数据。数据集包含用于构建该长短期记忆神经网络的代码、用于训练与测试该网络的一维二维水动力HEC-RAS模型数据集，以及用于运行代码与可视化结果的辅助文件。本数据集的详细说明详见Readme文件。若对本数据集存在任何进一步疑问，请联系论文作者。