Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Peiman Parisouj"'
Publikováno v:
Results in Engineering, Vol 24, Iss , Pp 103319- (2024)
Accurate runoff forecasting is crucial for effective water resource management, yet existing models often face challenges due to the complexity of hydrological systems. This study addresses these challenges by introducing a novel bio-inspired metaheu
Externí odkaz:
https://doaj.org/article/fc6460149fa94209a82c118c64e76fbd
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 17, Iss 1 (2023)
This paper presents a novel approach for enhancing long-term runoff simulations through the integration of empirical mode decomposition (EMD) with four machine learning (ML) models: ensemble, support vector machine (SVM), convolutional neural network
Externí odkaz:
https://doaj.org/article/b9e283c0b7814b00a8faefd2d0da5585
Autor:
Peiman Parisouj, Esmaiil Mokari, Hamid Mohebzadeh, Hamid Goharnejad, Changhyun Jun, Jeill Oh, Sayed M. Bateni
Publikováno v:
Applied Sciences, Vol 12, Iss 15, p 7464 (2022)
Accurate rainfall-runoff modeling is crucial for water resource management. However, the available models require more field-measured data to produce accurate results, which has been a long-term issue in hydrological modeling. Machine learning (ML) m
Externí odkaz:
https://doaj.org/article/ab78baf63741449683d0ab25f095b1bf
Publikováno v:
Journal of Applied Water Engineering and Research. 9:161-174
This study investigates the performance of TRMM and PERSIANN satellite rainfall data as input in a reliable rainfall-runoff model in order to provide information to the flood early warning for the ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02b413ca7a6abf6797d3df7a490b83dc
https://doi.org/10.1080/23249676.2021.1884617
https://doi.org/10.1080/23249676.2021.1884617
Publikováno v:
Water Resources Management. 34:4113-4131
Streamflow estimation plays a significant role in water resources management, especially for flood mitigation, drought warning, and reservoir operation. Hence, the current study examines the prediction capability of three well-known machine learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f3911ba050ed25ed9e8a30640eec4a2c
https://doi.org/10.1007/s11269-020-02659-5
https://doi.org/10.1007/s11269-020-02659-5