{
  "id": "2209.08729",
  "version": "v1",
  "published": "2022-09-19T02:58:31.000Z",
  "updated": "2022-09-19T02:58:31.000Z",
  "title": "Data-driven and machine-learning based prediction of wave propagation behavior in dam-break flood",
  "authors": [
    "Changli Li",
    "Zheng Han",
    "Yange Li",
    "Ming Li",
    "Weidong Wang"
  ],
  "categories": [
    "physics.flu-dyn",
    "stat.ML"
  ],
  "abstract": "The computational prediction of wave propagation in dam-break floods is a long-standing problem in hydrodynamics and hydrology. Until now, conventional numerical models based on Saint-Venant equations are the dominant approaches. Here we show that a machine learning model that is well-trained on a minimal amount of data, can help predict the long-term dynamic behavior of a one-dimensional dam-break flood with satisfactory accuracy. For this purpose, we solve the Saint-Venant equations for a one-dimensional dam-break flood scenario using the Lax-Wendroff numerical scheme and train the reservoir computing echo state network (RC-ESN) with the dataset by the simulation results consisting of time-sequence flow depths. We demonstrate a good prediction ability of the RC-ESN model, which ahead predicts wave propagation behavior 286 time-steps in the dam-break flood with a root mean square error (RMSE) smaller than 0.01, outperforming the conventional long short-term memory (LSTM) model which reaches a comparable RMSE of only 81 time-steps ahead. To show the performance of the RC-ESN model, we also provide a sensitivity analysis of the prediction accuracy concerning the key parameters including training set size, reservoir size, and spectral radius. Results indicate that the RC-ESN are less dependent on the training set size, a medium reservoir size K=1200~2600 is sufficient. We confirm that the spectral radius \\r{ho} shows a complex influence on the prediction accuracy and suggest a smaller spectral radius \\r{ho} currently. By changing the initial flow depth of the dam break, we also obtained the conclusion that the prediction horizon of RC-ESN is larger than that of LSTM.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-09-19T02:58:31.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "prediction",
      "computing echo state network",
      "spectral radius",
      "one-dimensional dam-break flood",
      "ahead predicts wave propagation behavior"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}