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Enhancing the performance of deep learning technique by combining with gradient boosting in rainfall-runoff simulation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Wydawca
Rocznik
Tom
Strony
216--223
Opis fizyczny
Bibliogr. 22 poz., mapa, tab., wykr.
Twórcy
  • Tashkent State Pedagogical University, Faculty of Math and Physics, 27 Bunyodkor Ave, 100070, Tashkent, Uzbekistan
Bibliografia
  • Agaltseva, N. et al. (2011) “Estimating hydrological characteristics in the Amu Darya River basin under climate change conditions,” Russian Meteorology and Hydrology, 36(10), pp. 681–689. Available at: https://doi.org/10.3103/s1068373911100062.
  • Aoulmi, Y. et al. (2023) “Runoff predictions in a semiarid watershed by convolutional neural networks improved with metaheuristic algorithms and forced with reanalysis and climate data,” Journal of Hydrologic Engineering, 28(7). Available at: https://doi.org/10.1061/jhyeff.heeng-5920.
  • Ardabili, S. et al. (2020) “Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review,” in A.R Várkonyi-Kóczy (ed.) Engineering for Sustainable Future: Selected papers of the 18th International Conference on Global Research and Education Inter-Academia–2019. Lecture notes in networks and systems, 101. Cham: Springer. pp. 52–62. Available at: https://doi.org/10.1007/978-3-030-36841-8_5.
  • Beddal, D., Achite, M. and Baahmed, D. (2020) “Streamflow prediction using data-driven models: Case study of Wadi Hounet, north-western Algeria,” Journal of Water and Land Development, 47, pp. 16–24. Available at: https://doi.org/10.24425/jwld.2020.135027.
  • Cichocki, A. et al. (2018) “Deep learning: Theory and practice,” Bulletin of the Polish Academy of Sciences: Technical Sciences, 66, pp. 757–759. Available at: https://doi.org/10.24425/bpas.2018.125923.
  • Ghobadi, F. and Kang, D. (2023) “Application of machine learning in water resources management: A systematic literature review,” Water, 15(4), 620. Available at: https://doi.org/10.3390/w15040620.
  • Khosravi, M., Afshar, A. and Molajou, A. (2022) “Decision tree-based conditional operation rules for optimal conjunctive use of surface and groundwater,” Water Resources Management, 36(6), pp. 2013–2025. Available at: https://doi.org/10.1007/s11269-022-03123-2.
  • Molajou, A. et al. (2021) “Optimal design and feature selection by genetic algorithm for emotional artificial neural network (EANN) in rainfall-runoff modeling,” Water Resources Management, 35(8), pp. 2369–2384. Available at: https://doi.org/10.1007/s11269-021-02818-2.
  • Ni, L. et al. (2020) “Streamflow forecasting using extreme gradient boosting model coupled with Gaussian mixture model,” Journal of Hydrology, 586, 124901. Available at: https://doi.org/10.1016/j.jhydrol.2020.124901.
  • Nourani, V. et al. (2019) “Hybrid wavelet-M5 model tree for rainfall-runoff modeling,” Journal of Hydrologic Engineering, 24(5). Available at: https://doi.org/10.1061/(asce)he.1943-5584.0001777.
  • Nourani, V., Tajbakhsh, A.D. and Molajou, A. (2018) “Data mining based on wavelet and decision tree for rainfall-runoff simulation,” Hydrology Research, 50(1), pp. 75–84. Available at: https://doi.org/10.2166/nh.2018.049.
  • Obasi, A.A. et al. (2020) “Rainfall-river discharge modelling for flood forecasting using Artificial Neural Network (ANN),” Journal of Water and Land Development, 44, pp. 98–105. Available at: https://doi.org/10.24425/jwld.2019.127050.
  • Rezaeianjouybari, B. and Shang, Y. (2020) “Deep learning for prognostics and health management: State of the art, challenges, and opportunities,” Measurement, 163, 107929. Available at: https://doi.org/10.1016/j.measurement.2020.107929.
  • Sanders, W.M. et al. (2022) “Data-driven flood alert system (FAS) using extreme gradient boosting (XGBOOST) to forecast flood stages,” Water, 14(5), 747. Available at: https://doi.org/10.3390/w14050747.
  • Saufi, S.R. et al. (2019) “Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: A review,” IEEE Access, 7, pp. 122644–122662. Available at: https://doi.org/10.1109/access.2019.2938227.
  • Sharghi, E. et al. (2018) “Emotional ANN (EANN) and wavelet-ANN (WANN) approaches for Markovian and seasonal based modeling of rainfall-runoff process,” Water Resources Management, 32(10), pp. 3441–3456. Available at: https://doi.org/10.1007/s11269-018-2000-y.
  • Shen, C. and Lawson, K. (2021) “Applications of deep learning in hydrology,” in G. Camps-Valls, D. Tuia, X.X. Zhu, M. Reichstein (eds.) Deep learning for the earth sciences: A comprehensive approach to remote sensing, climate science, and geosciences. Hoboken: John Wiley & Sons, pp. 283–297. Available at: https://doi.org/10.1002/9781119646181.ch19.
  • Sit, M. et al. (2020) “A comprehensive review of deep learning applications in hydrology and water resources,” Water Science and Technology, 82(12), pp. 2635–2670. Available at: https://doi.org/10.2166/wst.2020.369.
  • Song, Z. et al. (2022) “Regionalization of hydrological model parameters using gradient boosting machine,” Hydrology and Earth System Sciences, 26(2), pp. 505–524. Available at: https://doi.org/10.5194/hess-26-505-2022.
  • Wegerich, K. (2008) “Hydro-hegemony in the Amu Darya Basin,” Water Policy, 10(S2), pp. 71–88. Available at: https://doi.org/10.2166/wp.2008.208.
  • Xu, K. et al. (2023) “Rapid prediction model for urban floods based on a light gradient boosting machine approach and hydrological–hydraulic model,” International Journal of Disaster Risk Science, 14(1), pp. 79–97. Available at: https://doi.org/10.1007/s13753-023-00465-2.
  • Zhu, S. et al. (2023) “Spatiotemporal deep learning rainfall-runoff forecasting combined with remote sensing precipitation products in large scale basins,” Journal of Hydrology, 616, 128727. Available at: https://doi.org/10.1016/j.jhydrol.2022.128727.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-23d56523-b89c-441d-a8d3-be50336303f3
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