Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Nazanin Behfar"'
Publikováno v:
Water Supply, Vol 22, Iss 1, Pp 707-733 (2022)
In this study, Artificial Intelligence (AI) models along with ensemble techniques were employed for predicting suspended sediment load (SSL) via single station and multi-station scenarios. Feed Forward Neural Networks (FFNNs), Adaptive Neuro-Fuzzy In
Externí odkaz:
https://doaj.org/article/64db4f7f3f674677ad779f2c09c1f1c3
Publikováno v:
Water, Vol 13, Iss 23, p 3384 (2021)
In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sou
Externí odkaz:
https://doaj.org/article/04d1bb1f973042e197532e660b310534
Publikováno v:
Atmosphere, Vol 10, Iss 2, p 80 (2019)
The aim of ensemble precipitation prediction in this paper was to achieve the best performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based modeling was proposed for prediction of monthly precipitation with three di
Externí odkaz:
https://doaj.org/article/53ed60d007b2472a8dbda5a1a5df6328
Publikováno v:
Handbook of Hydroinformatics ISBN: 9780128219614
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c9b5242add2d3deb25886d4ff199b28
https://doi.org/10.1016/b978-0-12-821961-4.00004-x
https://doi.org/10.1016/b978-0-12-821961-4.00004-x
Autor:
Farizul Nizam Abdullah, U. Dinesh Acharya, Mario J. Al Sayah, Nor Eliza Alias, Wafae Allaoui, Sheikh Hefzul Bari, M. Mehdi Bateni, Nazanin Behfar, Maroua Bouteffeha, Omar-Darío Cardona, Martha Liliana Carreño, Kironmala Chanda, Rim Cherif, Elisa Coraggio, Prabal Das, Khadija Diani, Hicham El Belrhiti, Saeid Eslamian, Said Ettazarini, Soheila Farzi, Emna Gargouri-Ellouze, Fatemeh Sohrabi Geshnigani, Hüseyin Gökçekuş, Mohammad Reza Golabi, AbdelAli Gourfi, Youssef Hahou, Dawei Han, Salim Heddam, Mohammad Jamali, T.R. Jayashree, José A. Junqueira Junior, Ozgur Kisi, Saravanan Kothadaraman, Mohan Kuppusamy, Taesam Lee, Anurag Malik, Vanessa A. Mantovani, Carlos R. Mello, José M. Mello, Kaoutar Mounir, Hessam Najafi, Vahid Nourani, Nardin Jabbarian Paknezhad, Dinagarapandi Pandi, Rasnavi Paramasivam, Saeideh Parvizi, N.V. Subba Reddy, André F. Rodrigues, Yaser Sabzevari, Fahreddin Sadikoglu, Saad Shauket Sammen, Daniel Schertzer, Elnaz Sharghi, Vijay P. Singh, Doudja Souag-Gamane, Marcela C.N.S. Terra, Yazid Tikhamarine, Theo Tryfonas, Ibrahim Khalil Umar, Pierre-Antoine Versini
Publikováno v:
Handbook of Hydroinformatics ISBN: 9780128219614
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfae743c54c4f15e171b37ac76c2e153
https://doi.org/10.1016/b978-0-12-821961-4.09992-9
https://doi.org/10.1016/b978-0-12-821961-4.09992-9
Publikováno v:
Water Supply. 22:707-733
In this study, Artificial Intelligence (AI) models along with ensemble techniques were employed for predicting suspended sediment load (SSL) via single station and multi-station scenarios. Feed Forward Neural Networks (FFNNs), Adaptive Neuro-Fuzzy In
Publikováno v:
Applied Energy. 315:119069
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030352486
This study aimed at time-space estimations of monthly precipitation via a two-stage modeling framework. In temporal modeling as the first stage, three different AI models were applied to observed precipitation data from seven stations located in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad3c7c63f1925e12d03f6cab8fc3d43b
https://doi.org/10.1007/978-3-030-35249-3_16
https://doi.org/10.1007/978-3-030-35249-3_16
Publikováno v:
Journal of Hydroinformatics. 20:1071-1084
In this paper, an ensemble artificial intelligence (AI) based model is proposed for seepage modeling. For this purpose, firstly several AI models (i.e. Feed Forward Neural Network, Support Vector Regression and Adaptive Neural Fuzzy Inference System)
Publikováno v:
Water, Vol 13, Iss 3384, p 3384 (2021)
In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sou