Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm
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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
|
| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/12/11/3015 |
| _version_ | 1797549661704159232 |
|---|---|
| author | Babak Mohammadi Yiqing Guan Pouya Aghelpour Samad Emamgholizadeh Ramiro Pillco Zolá Danrong Zhang |
| author_facet | Babak Mohammadi Yiqing Guan Pouya Aghelpour Samad Emamgholizadeh Ramiro Pillco Zolá Danrong Zhang |
| author_sort | Babak Mohammadi |
| collection | DOAJ |
| description | 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 <inline-formula><math display="inline"><semantics><mrow><msub><mi>X</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>,</mo><mo> </mo><msub><mi>X</mi><mrow><mi>t</mi><mo>−</mo><mn>2</mn></mrow></msub><mo>,</mo><mo> </mo><msub><mi>X</mi><mrow><mi>t</mi><mo>−</mo><mn>3</mn></mrow></msub><mo>,</mo><mo> </mo><msub><mi>X</mi><mrow><mi>t</mi><mo>−</mo><mn>4</mn></mrow></msub></mrow></semantics></math></inline-formula> 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 R<sup>2</sup> = 0.96). |
| first_indexed | 2024-03-10T15:18:34Z |
| format | Article |
| id | doaj.art-36b9f91315244cdfa9f069fb1f2597f5 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T15:18:34Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-36b9f91315244cdfa9f069fb1f2597f52023-11-20T18:43:01ZengMDPI AGWater2073-44412020-10-011211301510.3390/w12113015Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer AlgorithmBabak Mohammadi0Yiqing Guan1Pouya Aghelpour2Samad Emamgholizadeh3Ramiro Pillco Zolá4Danrong Zhang5College of Hydrology and Water Resources, Hohai University, Nanjing 210098, ChinaCollege of Hydrology and Water Resources, Hohai University, Nanjing 210098, ChinaMSc graduated of Agricultural Meteorology, Department of Water Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan 65178-38695, IranDepartment of Water and Environmental Engineering, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood 36199-95161, IranInstituto de Hidráulica e Hidrología, Universidad Mayor de San Andrés, La Paz 699, BoliviaCollege of Hydrology and Water Resources, Hohai University, Nanjing 210098, ChinaLakes 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 <inline-formula><math display="inline"><semantics><mrow><msub><mi>X</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>,</mo><mo> </mo><msub><mi>X</mi><mrow><mi>t</mi><mo>−</mo><mn>2</mn></mrow></msub><mo>,</mo><mo> </mo><msub><mi>X</mi><mrow><mi>t</mi><mo>−</mo><mn>3</mn></mrow></msub><mo>,</mo><mo> </mo><msub><mi>X</mi><mrow><mi>t</mi><mo>−</mo><mn>4</mn></mrow></msub></mrow></semantics></math></inline-formula> 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 R<sup>2</sup> = 0.96).https://www.mdpi.com/2073-4441/12/11/3015lake water levelpredictiondata-driven techniqueshybrid modelsupport vector regressionTiticaca Lake |
| spellingShingle | Babak Mohammadi Yiqing Guan Pouya Aghelpour Samad Emamgholizadeh Ramiro Pillco Zolá Danrong Zhang Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm Water lake water level prediction data-driven techniques hybrid model support vector regression Titicaca Lake |
| title | Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm |
| title_full | Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm |
| title_fullStr | Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm |
| title_full_unstemmed | Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm |
| title_short | Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm |
| title_sort | simulation of titicaca lake water level fluctuations using hybrid machine learning technique integrated with grey wolf optimizer algorithm |
| topic | lake water level prediction data-driven techniques hybrid model support vector regression Titicaca Lake |
| url | https://www.mdpi.com/2073-4441/12/11/3015 |
| work_keys_str_mv | AT babakmohammadi simulationoftiticacalakewaterlevelfluctuationsusinghybridmachinelearningtechniqueintegratedwithgreywolfoptimizeralgorithm AT yiqingguan simulationoftiticacalakewaterlevelfluctuationsusinghybridmachinelearningtechniqueintegratedwithgreywolfoptimizeralgorithm AT pouyaaghelpour simulationoftiticacalakewaterlevelfluctuationsusinghybridmachinelearningtechniqueintegratedwithgreywolfoptimizeralgorithm AT samademamgholizadeh simulationoftiticacalakewaterlevelfluctuationsusinghybridmachinelearningtechniqueintegratedwithgreywolfoptimizeralgorithm AT ramiropillcozola simulationoftiticacalakewaterlevelfluctuationsusinghybridmachinelearningtechniqueintegratedwithgreywolfoptimizeralgorithm AT danrongzhang simulationoftiticacalakewaterlevelfluctuationsusinghybridmachinelearningtechniqueintegratedwithgreywolfoptimizeralgorithm |