Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods
Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with partic...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/23/3379 |
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| author | Rana Muhammad Adnan Reham R. Mostafa Abu Reza Md. Towfiqul Islam Alireza Docheshmeh Gorgij Alban Kuriqi Ozgur Kisi |
| author_facet | Rana Muhammad Adnan Reham R. Mostafa Abu Reza Md. Towfiqul Islam Alireza Docheshmeh Gorgij Alban Kuriqi Ozgur Kisi |
| author_sort | Rana Muhammad Adnan |
| collection | DOAJ |
| description | Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle swarm optimization (PSO), the genetic algorithm (GA), the grey wolf optimization (GWO), the social spider optimization (SSO), the salp swarm algorithm (SSA) and the hunger games search algorithm (HGS) were used to forecast droughts based on the standard precipitation index (SPI). Monthly precipitation data from three stations in Bangladesh were used in the applications. The accuracy of the methods was compared by forecasting four SPI indices, SPI3, SPI6, SPI9, and SPI12, using the root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R<sup>2</sup>). The HGS algorithm provided a better performance than the alternative algorithms, and it considerably improved the accuracy of the RVFL method in drought forecasting; the improvement in RMSE for the SPI3, SP6, SPI9, and SPI12 was by 6.14%, 11.89%, 14.14%, 24.5% in station 1, by 6.02%, 17.42%, 13.49%, 24.86% in station 2 and by 7.55%, 26.45%, 15.27%, 13.21% in station 3, respectively. The outcomes of the study recommend the use of a HGS-based RVFL in drought modeling. |
| first_indexed | 2024-03-10T04:42:58Z |
| format | Article |
| id | doaj.art-e28b932f2e66421ebec43f1e56c6aff6 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T04:42:58Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-e28b932f2e66421ebec43f1e56c6aff62023-11-23T03:14:39ZengMDPI AGWater2073-44412021-12-011323337910.3390/w13233379Improving Drought Modeling Using Hybrid Random Vector Functional Link MethodsRana Muhammad Adnan0Reham R. Mostafa1Abu Reza Md. Towfiqul Islam2Alireza Docheshmeh Gorgij3Alban Kuriqi4Ozgur Kisi5School of Economics and Statistics, Guangzhou University, Guangzhou 510006, ChinaInformation Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, EgyptDepartment of Disaster Management, Begum Rokeya University, Rangpur 5400, BangladeshFaculty of Industry and Mining (Khash), University of Sistan and Baluchestan, Zahedan 9816745845, IranCERIS, Instituto Superior Tecnico, Universidade de Lisboa, 1049-001 Lisbon, PortugalDepartment of Civil Engineering, University of Applied Sciences, 23562 Lübeck, GermanyDrought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle swarm optimization (PSO), the genetic algorithm (GA), the grey wolf optimization (GWO), the social spider optimization (SSO), the salp swarm algorithm (SSA) and the hunger games search algorithm (HGS) were used to forecast droughts based on the standard precipitation index (SPI). Monthly precipitation data from three stations in Bangladesh were used in the applications. The accuracy of the methods was compared by forecasting four SPI indices, SPI3, SPI6, SPI9, and SPI12, using the root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R<sup>2</sup>). The HGS algorithm provided a better performance than the alternative algorithms, and it considerably improved the accuracy of the RVFL method in drought forecasting; the improvement in RMSE for the SPI3, SP6, SPI9, and SPI12 was by 6.14%, 11.89%, 14.14%, 24.5% in station 1, by 6.02%, 17.42%, 13.49%, 24.86% in station 2 and by 7.55%, 26.45%, 15.27%, 13.21% in station 3, respectively. The outcomes of the study recommend the use of a HGS-based RVFL in drought modeling.https://www.mdpi.com/2073-4441/13/23/3379drought modelingstandard precipitation indexrandom vector functional linkhunger games search algorithm |
| spellingShingle | Rana Muhammad Adnan Reham R. Mostafa Abu Reza Md. Towfiqul Islam Alireza Docheshmeh Gorgij Alban Kuriqi Ozgur Kisi Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods Water drought modeling standard precipitation index random vector functional link hunger games search algorithm |
| title | Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods |
| title_full | Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods |
| title_fullStr | Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods |
| title_full_unstemmed | Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods |
| title_short | Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods |
| title_sort | improving drought modeling using hybrid random vector functional link methods |
| topic | drought modeling standard precipitation index random vector functional link hunger games search algorithm |
| url | https://www.mdpi.com/2073-4441/13/23/3379 |
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