Hybrid data intelligent models and applications for water level prediction

Yaseen, Zaher Mundher and Deo, Ravinesh C. and Ebtehaj, Isa and Bonakdari, Hossein (2018) Hybrid data intelligent models and applications for water level prediction. In: Handbook of research on predictive modeling and optimization methods in science and engineering. Advances in Computational Intelligence and Robotics (ACIR) Book Series. IGI Publishing (IGI Global), Hershey, United States, pp. 121-139. ISBN 9781522547662

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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.


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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published chapter deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 07 Aug 2018 05:33
Last Modified: 24 Sep 2018 05:49
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.4018/978-1-5225-4766-2.ch006
URI: http://eprints.usq.edu.au/id/eprint/34658

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