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Open AccessArticle

Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm

1
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
2
MSc graduated of Agricultural Meteorology, Department of Water Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan 65178-38695, Iran
3
Department of Water and Environmental Engineering, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood 36199-95161, Iran
4
Instituto de Hidráulica e Hidrología, Universidad Mayor de San Andrés, La Paz 699, Bolivia
*
Authors to whom correspondence should be addressed.
Water 2020, 12(11), 3015; https://doi.org/10.3390/w12113015
Received: 27 July 2020 / Revised: 21 October 2020 / Accepted: 23 October 2020 / Published: 27 October 2020
Lakes have an important role in storing water for drinking, producing hydroelectric power, and environmental, agricultural, and industrial uses. In order to optimize the use of lakes, precise prediction of the lake water level (LWL) is a main issue in water resources management. Due to the existence of nonlinear relations, uncertainty, and characteristics of the time series variables, the exact prediction of the lake water level is difficult. In this study the hybrid support vector regression (SVR) and the grey wolf algorithm (GWO) are used to predict lake water level fluctuations. Also, three types of data preprocessing methods, namely Principal component analysis, Random forest, and Relief algorithm were used for finding the best input variables for prediction LWL by the SVR and SVR-GWO models. Before the LWL simulation on monthly time step using the hybrid model, an evolutionary approach based on different monthly lags was conducted for determining the best mask of the input variables. Results showed that based on the random forest method, the best scenario of the inputs was Xt1, Xt2, Xt3, Xt4 for the SVR-GWO model. Also, the performance of the SVR-GWO model indicated that it could simulate the LWL with acceptable accuracy (with RMSE = 0.08 m, MAE = 0.06 m, and R2 = 0.96). View Full-Text
Keywords: lake water level; prediction; data-driven techniques; hybrid model; support vector regression; Titicaca Lake lake water level; prediction; data-driven techniques; hybrid model; support vector regression; Titicaca Lake
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MDPI and ACS Style

Mohammadi, B.; Guan, Y.; Aghelpour, P.; Emamgholizadeh, S.; Pillco Zolá, R.; Zhang, D. Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm. Water 2020, 12, 3015. https://doi.org/10.3390/w12113015

AMA Style

Mohammadi B, Guan Y, Aghelpour P, Emamgholizadeh S, Pillco Zolá R, Zhang D. Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm. Water. 2020; 12(11):3015. https://doi.org/10.3390/w12113015

Chicago/Turabian Style

Mohammadi, Babak; Guan, Yiqing; Aghelpour, Pouya; Emamgholizadeh, Samad; Pillco Zolá, Ramiro; Zhang, Danrong. 2020. "Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm" Water 12, no. 11: 3015. https://doi.org/10.3390/w12113015

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