Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms

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Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms

Pham, Quoc Bao (author): Thu Dau Mot University

Mohammadi, Babak (author): Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science

Moazenzadeh, Roozbeh (author): Shahrood University of Technology

Heddam, Salim (author)

Zolá, Ramiro Pillco (author): Higher University of San Andrés

Sankaran, Adarsh (author): TKM College of Engineering

Gupta, Vivek (author): Indian Institute of Technology, Roorkee

Elkhrachy, Ismail (author): Najran University

Khedher, Khaled Mohamed (author): High Institute of Technological Studies,King Khalid University

Anh, Duong Tran (author): Van Lang University

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(creator_code:org_t)

2022-11-21
2023
English.
In: Applied water science. - : Springer Science and Business Media LLC. - 2190-5487 .- 2190-5495. ; 13:1

Related links:: http://dx.doi.org/10... (free); show more...; https://lup.lub.lu.s...; https://doi.org/10.1...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were Xt-1,Xt-2,Xt-3,Xt-4,Xt-12) the ANFIS-WOA model attained root mean square error (RMSE ≈ 0.08 m), mean absolute error (MAE ≈ 0.06 m), and coefficient of determination (R2≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

Subject headings

NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)
TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Water Engineering (hsv//eng)
NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)

Keyword

Freshwater management
Hybrid model
Lake water-level prediction
Metaheuristics algorithms
South America
Surface water

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art (subject category)
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By the author/editor: Pham, Quoc Bao; Mohammadi, Babak; Moazenzadeh, Roo ...; Heddam, Salim; Zolá, Ramiro Pil ...; Sankaran, Adarsh; show more...; Gupta, Vivek; Elkhrachy, Ismai ...; Khedher, Khaled ...; Anh, Duong Tran; show less...

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NATURAL SCIENCES: NATURAL SCIENCES; and Earth and Relate ...; and Physical Geograp ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Civil Engineerin ...; and Water Engineerin ...

NATURAL SCIENCES: NATURAL SCIENCES; and Earth and Relate ...; and Oceanography Hyd ...

Articles in the publication: Applied water sc ...

By the university: Lund University

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