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    Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism

    Source: Journal of Hydrologic Engineering:;2024:;Volume ( 029 ):;issue: 006::page 04024042-1
    Author:
    Marjan Kordani
    ,
    Mohammad Reza Nikoo
    ,
    Mahmood Fooladi
    ,
    Iman Ahmadianfar
    ,
    Rouzbeh Nazari
    ,
    Amir H. Gandomi
    DOI: 10.1061/JHYEFF.HEENG-6262
    Publisher: American Society of Civil Engineers
    Abstract: Floods, as major natural disasters, cause massive property destruction and death. Understanding the occurrence time of this event by advance notice helps consider operational flood prevention systems and platforms. Precise flood forecasting provides a suitable time for policymakers and the public to consider helpful responses to this event. This study introduces an innovative methodology to enhance the precision of long-term flood predictions by employing a multistep forecasting approach. Our approach leverages historical time-series data on precipitation and streamflow to train an autoencoder algorithm. The primary objective is to develop advanced forecasting models to predict 12-step-weekly ahead flood occurrences during the critical April to July period from 2019 to 2021 within the DuPage River basin, Illinois, USA. In order to achieve this goal, we explore three deep learning techniques: bidirectional long short-term memory (BI-LSTM), ensemble long short-term memory (E-LSTM), and ensemble long short-term memory-gated recurrent unit (E-LSTM-GRU). Then, we integrate an attention mechanism (AM) that utilizes dynamic fusion techniques to emphasize the salient features of ensemble models. A dedicated fusion model is developed for each forecasting stage, effectively consolidating the predictions from various deep-learning models. Additionally, two traditional machine learning techniques, namely MLP and SVM models, are used to compare and justify the efficiency of applied deep learning models. The performance evaluation of our approach using statistical error metrics, including coefficient of determination (R2), normalized root mean square error, normalized mean absolute error, mean absolute percentage error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias, for the 12th step prediction reveals impressive results, with average values of 0.976, 2.393, 1.892, 20.956, 0.967, 0.923, and 2.307, respectively. These findings underscore the capability of our proposed models to significantly reduce uncertainty in flood forecasting, thus enhancing the reliability and accuracy of future predictions.
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      Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4304767
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    contributor authorMarjan Kordani
    contributor authorMohammad Reza Nikoo
    contributor authorMahmood Fooladi
    contributor authorIman Ahmadianfar
    contributor authorRouzbeh Nazari
    contributor authorAmir H. Gandomi
    date accessioned2025-04-20T10:27:42Z
    date available2025-04-20T10:27:42Z
    date copyright9/14/2024 12:00:00 AM
    date issued2024
    identifier otherJHYEFF.HEENG-6262.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304767
    description abstractFloods, as major natural disasters, cause massive property destruction and death. Understanding the occurrence time of this event by advance notice helps consider operational flood prevention systems and platforms. Precise flood forecasting provides a suitable time for policymakers and the public to consider helpful responses to this event. This study introduces an innovative methodology to enhance the precision of long-term flood predictions by employing a multistep forecasting approach. Our approach leverages historical time-series data on precipitation and streamflow to train an autoencoder algorithm. The primary objective is to develop advanced forecasting models to predict 12-step-weekly ahead flood occurrences during the critical April to July period from 2019 to 2021 within the DuPage River basin, Illinois, USA. In order to achieve this goal, we explore three deep learning techniques: bidirectional long short-term memory (BI-LSTM), ensemble long short-term memory (E-LSTM), and ensemble long short-term memory-gated recurrent unit (E-LSTM-GRU). Then, we integrate an attention mechanism (AM) that utilizes dynamic fusion techniques to emphasize the salient features of ensemble models. A dedicated fusion model is developed for each forecasting stage, effectively consolidating the predictions from various deep-learning models. Additionally, two traditional machine learning techniques, namely MLP and SVM models, are used to compare and justify the efficiency of applied deep learning models. The performance evaluation of our approach using statistical error metrics, including coefficient of determination (R2), normalized root mean square error, normalized mean absolute error, mean absolute percentage error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias, for the 12th step prediction reveals impressive results, with average values of 0.976, 2.393, 1.892, 20.956, 0.967, 0.923, and 2.307, respectively. These findings underscore the capability of our proposed models to significantly reduce uncertainty in flood forecasting, thus enhancing the reliability and accuracy of future predictions.
    publisherAmerican Society of Civil Engineers
    titleImproving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism
    typeJournal Article
    journal volume29
    journal issue6
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/JHYEFF.HEENG-6262
    journal fristpage04024042-1
    journal lastpage04024042-19
    page19
    treeJournal of Hydrologic Engineering:;2024:;Volume ( 029 ):;issue: 006
    contenttypeFulltext
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