Prediction of Water-Level in the Urmia Lake Using the Extreme Learning Machine Approach

Jalal Shiri (), Shahaboddin Shamshirband (), Ozgur Kisi, Sepideh Karimi, Seyyed M Bateni, Seyed Hossein Hosseini Nezhad and Arsalan Hashemi
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Jalal Shiri: University of Tabriz
Ozgur Kisi: Canik Basari University
Sepideh Karimi: University of Tabriz
Seyyed M Bateni: University of Hawaii
Seyed Hossein Hosseini Nezhad: Islamic Azad University
Arsalan Hashemi: University of Tabriz

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 14, No 16, 5217-5229

Abstract: Abstract Predicting the dynamics of water-level in lakes plays a vital role in navigation, water resources planning and catchment management. In this paper, the Extreme Learning Machine (ELM) approach was used to predict the daily water-level in the Urmia Lake. Daily water-level data from the Urmia Lake in northwest of Iran were used to train, test and validate the employed models. Results showed that the ELM approach can accurately forecast the water-level in the Urmia Lake. Outcomes from the ELM model were also compared with those of genetic programming (GP) and artificial neural networks (ANNs). It was found that the ELM technique outperforms GP and ANN in predicting water-level in the Urmia Lake. It also can learn the relation between the water-level and its influential variables much faster than the GP and ANN. Overall, the results show that the ELM approach can be used to predict dynamics of water-level in lakes.

Keywords: Extreme Learning Machine (ELM); Water level; Time series; Urmia Lake (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11269-016-1480-x

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