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    Wyświetlanie 1-2 z 2
    Tytuł:
    Performance evaluation of HEC-HMS model for continuous runoff simulation of Gilgel Gibe watershed, Southwest Ethiopia
    Autorzy:
    Fanta, Sewmehon Sisay
    Feyissa, Tolera Abdissa
    Powiązania:
    https://bibliotekanauki.pl/articles/1844290.pdf
    Data publikacji:
    2021
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    calibration
    Gilgel Gibe
    HEC-HMS
    runoff simulation
    sensitivity analysis
    validation
    Opis:
    Hydrological models are widely used for runoff simulation throughout the world. The objective of this study is to check the performance of the HEC-HMS model for continuous runoff simulation of Gilgel Gibe watershed. It includes sensitivity analysis, calibration, and validation. The model calibration was conducted with data from the year 1991 to 2002 and validated for the year 2003 to 2013 period using daily observed stream flow near the outlet of the watershed. To check the consistency of the model, both the calibration and validation periods were divided into two phases. The sensitivity analysis of parameters showed that curve number (CN) and wave travel time (K) were the most sensitive, whereas channel storage coefficient (x) and lag time (tlag) were moderately sensitive. The model performance measured using Nash–Sutcliff Efficiency (NSE), Percentage of Bias (PBIAS), correlation coefficient (R2), root mean square error (RMSE), and Percentage Error in Peak (PEP). The respective values were 0.795, 8.225%, 0.916, 27.105 m3∙s–1 and 7.789% during calibration, and 0.795, 23.015%, 0.916, 29.548 m3∙s–1 and –19.698% during validation. The result indicates that the HEC-HMS model well estimated the daily runoff and peak discharge of Gilgel Gibe watershed. Hence, the model is recommended for continuous runoff simulation of Gilgel Gibe watershed. The study will be helpful for efficient water resources and watershed management for Gilgel Gibe watershed. It can also be used as a reference or an input for any future hydrological investigations in the nearby un-gauged or poorly gauged watershed.
    Źródło:
    Journal of Water and Land Development; 2021, 50; 85-97
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Enhancing the performance of deep learning technique by combining with gradient boosting in rainfall-runoff simulation
    Autorzy:
    Abdullaeva, Barno S.
    Powiązania:
    https://bibliotekanauki.pl/articles/28411647.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    deep learning
    gradient boosting
    hybrid model
    multi-step ahead forecasting
    rainfall-runoff simulation
    Opis:
    Artificial neural networks are widely employed as data mining methods by researchers across various fields, including rainfall-runoff (R-R) statistical modelling. To enhance the performance of these networks, deep learning (DL) neural networks have been developed to improve modelling accuracy. The present study aims to improve the effectiveness of DL networks in enhancing the performance of artificial neural networks via merging with the gradient boosting (GB) technique for daily runoff data forecasting in the river Amu Darya, Uzbekistan. The obtained results showed that the new hybrid proposed model performed exceptionally well, achieving a 16.67% improvement in determination coefficient (R2) and a 23.18% reduction in root mean square error (RMSE) during the training phase compared to the single DL model. Moreover, during the verification phase, the hybrid model displayed remarkable performance, demonstrating a 66.67% increase in R2 and a 50% reduction in RMSE. Furthermore, the hybrid model outperformed the single GB model by a significant margin. During the training phase, the new model showed an 18.18% increase in R2 and a 25% reduction in RMSE. In the verification phase, it improved by an impressive 75% in R2 and a 33.33% reduction in RMSE compared to the single GB model. These findings highlight the potential of the hybrid DL-GB model in improving daily runoff data forecasting in the challenging hydrological context of the Amu Darya River basin in Uzbekistan.
    Źródło:
    Journal of Water and Land Development; 2023, 59; 216--223
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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