Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis

Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis

In the present study, an improved adaptive neuro fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models are hybridized with a sunflower optimization (SO) algorithm and are introduced for lake water level simulation. The Urmia Lake water level is predicted and assessed using the potent...

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Bibliographic Details
Main Authors: Mohammad Ehteram, Ahmad Ferdowsi, Mahtab Faramarzpour, Ahmed Mohammed Sami Al-Janabi, Nadhir Al-Ansari, Neeraj Dhanraj Bokde, Zaher Mundher Yaseen
Format: Article
Language:English
Published: Elsevier 2021-04-01
Series:Alexandria Engineering Journal
Subjects:
Hybrid artificial intelligence
Urmia Lake
Water level prediction
Machine learning
Water resources
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016820306840

Internet

http://www.sciencedirect.com/science/article/pii/S1110016820306840

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