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Title: | Artificial intelligence based models for stream-flow forecasting: 2000–2015 |
Authors: | Mundher Yaseen, Zaher El-Shafie, Ahmed Jaafar, Othman Afan, Haitham Abdulmohsin Sayl, Khamis Naba |
Keywords: | Artificial intelligence Stream-flow forecasting Fast orthogonal search Swarm intelligence |
Issue Date: | 22-Oct-2015 |
Abstract: | The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach |
URI: | http://localhost:8080/xmlui/handle/123456789/7325 |
Appears in Collections: | هندسة السدود والموارد المائية |
Files in This Item:
File | Description | Size | Format | |
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Artificial intelligence based models for stream-flow forecasting 2000–2015.pdf | 64.65 kB | Adobe PDF | View/Open |
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