Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms
Pham, Quoc Bao ; Mohammadi, Babak LU
; Moazenzadeh, Roozbeh
; Heddam, Salim
; Zolá, Ramiro Pillco
LU
; Sankaran, Adarsh
; Gupta, Vivek
; Elkhrachy, Ismail
; Khedher, Khaled Mohamed
and Anh, Duong Tran
(2023)
In Applied water science
13(1).
- Abstract
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... (More)
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.
(Less)
- author
- Pham, Quoc Bao
; Mohammadi, Babak
LU
; Moazenzadeh, Roozbeh
; Heddam, Salim
; Zolá, Ramiro Pillco
LU
; Sankaran, Adarsh
; Gupta, Vivek
; Elkhrachy, Ismail
; Khedher, Khaled Mohamed
and Anh, Duong Tran
- organization
- publishing date
- 2023-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Freshwater management, Hybrid model, Lake water-level prediction, Metaheuristics algorithms, South America, Surface water
- in
- Applied water science
- volume
- 13
- issue
- 1
- article number
- 13
- publisher
- Springer
- external identifiers
-
- scopus:85142282978
- ISSN
- 2190-5487
- DOI
- 10.1007/s13201-022-01815-z
- language
- English
- LU publication?
- yes
- additional info
- Funding Information: The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program grant code (NU/RC/SERC/11/3). Publisher Copyright: © 2022, The Author(s).
- id
- 9d6da462-cbfe-41d9-a64e-1e2074c9f926
- date added to LUP
- 2022-11-29 15:21:28
- date last changed
- 2024-01-24 10:54:12
@article{9d6da462-cbfe-41d9-a64e-1e2074c9f926,
abstract = {{<p>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 (R<sup>2</sup>≈ 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.</p>}},
author = {{Pham, Quoc Bao and Mohammadi, Babak and Moazenzadeh, Roozbeh and Heddam, Salim and Zolá, Ramiro Pillco and Sankaran, Adarsh and Gupta, Vivek and Elkhrachy, Ismail and Khedher, Khaled Mohamed and Anh, Duong Tran}},
issn = {{2190-5487}},
keywords = {{Freshwater management; Hybrid model; Lake water-level prediction; Metaheuristics algorithms; South America; Surface water}},
language = {{eng}},
number = {{1}},
publisher = {{Springer}},
series = {{Applied water science}},
title = {{Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms}},
url = {{http://dx.doi.org/10.1007/s13201-022-01815-z}},
doi = {{10.1007/s13201-022-01815-z}},
volume = {{13}},
year = {{2023}},
}