Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction
Hybrid metaheuristic algorithm (MA), an advanced tool in the artificial intelligence field, provides precise reference evapotranspiration (ETo) prediction that is highly important for water resource availability and hydrological studies. However, hybrid MAs are quite scarcely used to predict ETo in...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/1/98 |
| _version_ | 1797447188661403648 |
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| author | Rana Muhammad Adnan Ikram Reham R. Mostafa Zhihuan Chen Abu Reza Md. Towfiqul Islam Ozgur Kisi Alban Kuriqi Mohammad Zounemat-Kermani |
| author_facet | Rana Muhammad Adnan Ikram Reham R. Mostafa Zhihuan Chen Abu Reza Md. Towfiqul Islam Ozgur Kisi Alban Kuriqi Mohammad Zounemat-Kermani |
| author_sort | Rana Muhammad Adnan Ikram |
| collection | DOAJ |
| description | Hybrid metaheuristic algorithm (MA), an advanced tool in the artificial intelligence field, provides precise reference evapotranspiration (ETo) prediction that is highly important for water resource availability and hydrological studies. However, hybrid MAs are quite scarcely used to predict ETo in the existing literature. To this end, the prediction abilities of two support vector regression (SVR) models coupled with three types of MAs including particle swarm optimization (PSO), grey wolf optimization (GWO), and gravitational search algorithm (GSA) were studied and compared with single SVR and SVR-PSO in predicting monthly ETo using meteorological variables as inputs. Data obtained from Rajshahi, Bogra, and Rangpur stations in the humid region, northwestern Bangladesh, was used for this purpose as a case study. The prediction precision of the proposed models was trained and tested using nine input combinations and assessed using root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The tested results revealed that the SVR-PSOGWO model outperformed the other applied soft computing models in predicting ETo in all input combinations, followed by the SVR-PSOGSA, SVR-PSO, and SVR. It was found that SVR-PSOGWO decreases the RMSE of SVR, SVR-PSO, and SVR-PSOGSA by 23%, 27%, 14%, 21%, 19%, and 5% in Rangpur and Bogra stations during the testing stage. The RMSE of the SVR, SVR-PSO, and SVR-PSOGSA reduced by 32%, 20%, and 3%, respectively, employing the SVR-PSOGWO for the Rajshahi Station. The proposed hybrid machine learning model has been recommended as a potential tool for monthly ETo prediction in a humid region and similar climatic regions worldwide. |
| first_indexed | 2024-03-09T13:52:14Z |
| format | Article |
| id | doaj.art-9c1dc49ed62f4b01b1d6644b252ec24b |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T13:52:14Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-9c1dc49ed62f4b01b1d6644b252ec24b2023-11-30T20:48:54ZengMDPI AGAgronomy2073-43952022-12-011319810.3390/agronomy13010098Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration PredictionRana Muhammad Adnan Ikram0Reham R. Mostafa1Zhihuan Chen2Abu Reza Md. Towfiqul Islam3Ozgur Kisi4Alban Kuriqi5Mohammad Zounemat-Kermani6School of Economics and Statistics, Guangzhou University, Guangzhou 510006, ChinaInformation Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, EgyptEngineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 431400, ChinaDepartment of Disaster Management, Begum Rokeya University, Rangpur 5400, BangladeshDepartment of Civil Engineering, Technical University of Lübeck, 23562 Lübeck, GermanyCERIS, Instituto Superior Técnico, Universidade de Lisboa, 53089 Lisbon, PortugalDepartment of Water Engineering, Shahid Bahonar University of Kerman, Kerman 93630, IranHybrid metaheuristic algorithm (MA), an advanced tool in the artificial intelligence field, provides precise reference evapotranspiration (ETo) prediction that is highly important for water resource availability and hydrological studies. However, hybrid MAs are quite scarcely used to predict ETo in the existing literature. To this end, the prediction abilities of two support vector regression (SVR) models coupled with three types of MAs including particle swarm optimization (PSO), grey wolf optimization (GWO), and gravitational search algorithm (GSA) were studied and compared with single SVR and SVR-PSO in predicting monthly ETo using meteorological variables as inputs. Data obtained from Rajshahi, Bogra, and Rangpur stations in the humid region, northwestern Bangladesh, was used for this purpose as a case study. The prediction precision of the proposed models was trained and tested using nine input combinations and assessed using root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The tested results revealed that the SVR-PSOGWO model outperformed the other applied soft computing models in predicting ETo in all input combinations, followed by the SVR-PSOGSA, SVR-PSO, and SVR. It was found that SVR-PSOGWO decreases the RMSE of SVR, SVR-PSO, and SVR-PSOGSA by 23%, 27%, 14%, 21%, 19%, and 5% in Rangpur and Bogra stations during the testing stage. The RMSE of the SVR, SVR-PSO, and SVR-PSOGSA reduced by 32%, 20%, and 3%, respectively, employing the SVR-PSOGWO for the Rajshahi Station. The proposed hybrid machine learning model has been recommended as a potential tool for monthly ETo prediction in a humid region and similar climatic regions worldwide.https://www.mdpi.com/2073-4395/13/1/98reference evapotranspirationprediction with limited datasupport vector regressionparticle swarm optimizationgrey wolf optimization |
| spellingShingle | Rana Muhammad Adnan Ikram Reham R. Mostafa Zhihuan Chen Abu Reza Md. Towfiqul Islam Ozgur Kisi Alban Kuriqi Mohammad Zounemat-Kermani Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction Agronomy reference evapotranspiration prediction with limited data support vector regression particle swarm optimization grey wolf optimization |
| title | Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction |
| title_full | Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction |
| title_fullStr | Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction |
| title_full_unstemmed | Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction |
| title_short | Advanced Hybrid Metaheuristic Machine Learning Models Application for Reference Crop Evapotranspiration Prediction |
| title_sort | advanced hybrid metaheuristic machine learning models application for reference crop evapotranspiration prediction |
| topic | reference evapotranspiration prediction with limited data support vector regression particle swarm optimization grey wolf optimization |
| url | https://www.mdpi.com/2073-4395/13/1/98 |
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