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Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
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Behrooz Keshtegar, Jamshid Piri, Waqas Ul Hussan, Kamran Ikram, Muhammad Yaseen, Ozgur Kisi, Rana Muhammad Adnan, Muhammad Adnan and Muhammad Waseem
Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The presen...
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Vsevolod Moreido, Boris Gartsman, Dimitri P. Solomatine and Zoya Suchilina
With more machine learning methods being involved in social and environmental research activities, we are addressing the role of available information for model training in model performance. We tested the abilities of several machine learning models for...
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Sultan Noman Qasem, Saeed Samadianfard, Hamed Sadri Nahand, Amir Mosavi, Shahaboddin Shamshirband and Kwok-wing Chau
In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) at Tabriz statio...
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Rana Muhammad Adnan, Zhongmin Liang, Xiaohui Yuan, Ozgur Kisi, Muhammad Akhlaq and Binquan Li
Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neur...
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