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Nourani, Vahid, Roushangar, Kiyoumars, Andalib, Gholamreza (2018) An inverse method for watershed change detection using hybrid conceptual and artificial intelligence approaches. Journal of Hydrology, 562. 371-384 doi:10.1016/j.jhydrol.2018.05.018

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Reference TypeJournal (article/letter/editorial)
TitleAn inverse method for watershed change detection using hybrid conceptual and artificial intelligence approaches
JournalJournal of Hydrology
AuthorsNourani, VahidAuthor
Roushangar, KiyoumarsAuthor
Andalib, GholamrezaAuthor
Year2018 (July)Volume562
PublisherElsevier BV
DOIdoi:10.1016/j.jhydrol.2018.05.018Search in ResearchGate
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Mindat Ref. ID7245896Long-form Identifiermindat:1:5:7245896:1
GUID0
Full ReferenceNourani, Vahid, Roushangar, Kiyoumars, Andalib, Gholamreza (2018) An inverse method for watershed change detection using hybrid conceptual and artificial intelligence approaches. Journal of Hydrology, 562. 371-384 doi:10.1016/j.jhydrol.2018.05.018
Plain TextNourani, Vahid, Roushangar, Kiyoumars, Andalib, Gholamreza (2018) An inverse method for watershed change detection using hybrid conceptual and artificial intelligence approaches. Journal of Hydrology, 562. 371-384 doi:10.1016/j.jhydrol.2018.05.018
In(2018) Journal of Hydrology Vol. 562. Elsevier BV

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Roushangar, Kiyoumars, Mehrabani, Fatemeh Vojoudi, Shiri, Jalal (2014) Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs). Journal of Hydrology, 514. 114-122 doi:10.1016/j.jhydrol.2014.03.065
Nourani, Vahid, Mousavi, Shahram (2016) Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method. Journal of Hydrology, 536. 10-25 doi:10.1016/j.jhydrol.2016.02.030
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Nourani, Vahid, Najafi, Hessam, Sharghi, Elnaz, Roushangar, Kiyoumars (2021) Application of Z-numbers to monitor drought using large-scale oceanic-atmospheric parameters. Journal of Hydrology, 598. 126198pp. doi:10.1016/j.jhydrol.2021.126198
Nourani, Vahid, Kisi, Özgür, Komasi, Mehdi (2011) Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology, 402 (1). 41-59 doi:10.1016/j.jhydrol.2011.03.002
Nourani, Vahid, Hosseini Baghanam, Aida, Adamowski, Jan, Kisi, Ozgur (2014) Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology, 514. 358-377 doi:10.1016/j.jhydrol.2014.03.057
Nourani, Vahid, Andalib, Gholamreza, DÄ…browska, Dominika (2017) Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds. Journal of Hydrology, 548. 170-183 doi:10.1016/j.jhydrol.2017.03.002
Nourani, Vahid, Andalib, Gholamreza, Sadikoglu, Fahreddin, Sharghi, Elnaz (2018) Cascade-based multi-scale AI approach for modeling rainfall-runoff process. Hydrology Research, 49 (4). 1191-1207 doi:10.2166/nh.2017.045
Shahnazi, Saman, Roushangar, Kiyoumars, Hashemi, Hossein (2025) A novel implementation of pre-processing approaches and hybrid kernel-based model for short- and long-term groundwater drought forecasting. Journal of Hydrology, 652. doi:10.1016/j.jhydrol.2025.132667
Roushangar, Kiyoumars, Akhgar, Samira, Salmasi, Farzin, Shiri, Jalal (2014) Modeling energy dissipation over stepped spillways using machine learning approaches. Journal of Hydrology, 508. 254-265 doi:10.1016/j.jhydrol.2013.10.053
Nourani, Vahid, Elkiran, Gozen, Abdullahi, Jazuli (2019) Multi-station artificial intelligence based ensemble modeling of reference evapotranspiration using pan evaporation measurements. Journal of Hydrology, 577. 123958pp. doi:10.1016/j.jhydrol.2019.123958
 
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