The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Babak Mohammadi

Doctoral student

Default user image.

Simulation of titicaca lake water level fluctuations using hybrid machine learning technique integrated with grey wolf optimizer algorithm

Author

  • Babak Mohammadi
  • Yiqing Guan
  • Pouya Aghelpour
  • Samad Emamgholizadeh
  • Ramiro Pillco Zolá
  • Danrong Zhang

Summary, in English

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 leveAl 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 Xt−1, Xt−2, Xt−3, Xt−4 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).

Publishing year

2020-11

Language

English

Pages

1-18

Publication/Series

Water

Volume

12

Issue

11

Document type

Journal article

Publisher

MDPI AG

Topic

  • Oceanography, Hydrology, Water Resources

Keywords

  • Data-driven techniques
  • Hybrid model
  • Lake water level
  • Prediction
  • Support vector regression
  • Titicaca Lake

Status

Published

ISBN/ISSN/Other

  • ISSN: 2073-4441