Hybrid data intelligent models and applications for water level prediction
Edited book (chapter)
Chapter Title | Hybrid data intelligent models and applications for water level prediction |
---|---|
Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 2177 |
Book Title | Handbook of research on predictive modeling and optimization methods in science and engineering |
Authors | Yaseen, Zaher Mundher (Author), Deo, Ravinesh C. (Author), Ebtehaj, Isa (Author) and Bonakdari, Hossein (Author) |
Editors | Kim, Dookie, Roy, Sanjiban Sekhar, Länsivaara, Tim, Deo, Ravinesh C. and Samui, Pijush |
Page Range | 121-139 |
Series | Advances in Computational Intelligence and Robotics (ACIR) Book Series |
Chapter Number | 6 |
Number of Pages | 19 |
Year | 2018 |
Publisher | IGI Global |
Place of Publication | Hershey, United States |
ISBN | 9781522547662 |
9781522547679 | |
ISSN | 2327-0411 |
2327-042X | |
Digital Object Identifier (DOI) | https://doi.org/10.4018/978-1-5225-4766-2.ch006 |
Web Address (URL) | https://www.igi-global.com/book/handbook-research-predictive-modeling-optimization/185480 |
Abstract | Artificial intelligence (AI) models have been successfully applied in modeling engineering problems, including civil, water resources, electrical, and structure. The originality of the presented chapter is to investigate a non-tuned machine learning algorithm, called self-adaptive evolutionary extreme learning machine (SaE-ELM), to formulate an expert prediction model. The targeted application of the SaE-ELM is the prediction of river water level. Developing such water level prediction and monitoring models are crucial optimization tasks in water resources management and flood prediction. The aims of this chapter are (1) to conduct a comprehensive survey for AI models in water level modeling, (2) to apply a relatively new ML algorithm (i.e., SaE-ELM) for modeling water level, (3) to examine two different time scales (e.g., daily and monthly), and (4) to compare the inspected model with the extreme learning machine (ELM) model for validation. In conclusion, the contribution of the current chapter produced an expert and highly optimized predictive model that can yield a high-performance accuracy. |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | National University of Malaysia |
School of Agricultural, Computational and Environmental Sciences | |
Razi University, Iran | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q4x18/hybrid-data-intelligent-models-and-applications-for-water-level-prediction
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