English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58196/91757 (63%)
Visitors : 13773974      Online Users : 37
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118841

    Title: Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting
    Authors: Kao, I.-F.;Zhou, Y.;Chang, L.-C.;Chang, F.-J.
    Keywords: Flood forecastEncoder-Decoder (ED) model;Recurrent neural network (RNN);Long Short-Term Memory (LSTM);Sequence-to-sequence
    Date: 2020-04
    Issue Date: 2020-07-02 12:10:24 (UTC+8)
    Publisher: Elsevier BV
    Abstract: Operational flood control systems depend on reliable and accurate forecasts with a suitable lead time to take necessary actions against flooding. This study proposed a Long Short-Term Memory based Encoder-Decoder (LSTM-ED) model for multi-step-ahead flood forecasting for the first time. The Shihmen Reservoir catchment in Taiwan constituted the case study. A total of 12,216 hourly hydrological data collected from 23 typhoon events were allocated into three datasets for model training, validation, and testing. The input sequence of the model contained hourly reservoir inflows and rainfall data (traced back to the previous 8 h) of ten gauge stations, and the output sequence stepped into 1- up to 6-hour-ahead reservoir inflow forecasts. A feed forward neural network-based Encoder-Decoder (FFNN-ED) model was established for comparison purposes. This study conducted model training a number of times with various initial weights to evaluate the accuracy, stability, and reliability of the constructed FFNN-ED and LSTM-ED models. The results demonstrated that both models, in general, could provide suitable multi-step ahead forecasts, and the proposed LSTM-ED model not only could effectively mimic the long-term dependence between rainfall and runoff sequences but also could make more reliable and accurate flood forecasts than the FFNN-ED model. Concerning the time delay between the time horizons of model inputs (rainfall) and model outputs (runoff), the impact assessment of this time-delay on model performance indicated that the LSTM-ED model achieved similar forecast performance when fed with antecedent rainfall either at a shorter horizon of 4 h in the past (T − 4) or at horizons longer than 7 h in the past (>T − 7). We conclude that the proposed LSTM-ED that translates and links the rainfall sequence with the runoff sequence can improve the reliability of flood forecasting and increase the interpretability of model internals.
    Relation: Journal of Hydrology 583, 124631
    DOI: 10.1016/j.jhydrol.2020.124631
    Appears in Collections:[水資源及環境工程學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback