{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T07:18:20Z","timestamp":1769152700195,"version":"3.49.0"},"reference-count":68,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>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\u2013Sutcliffe efficiency (NSE), and the determination coefficient (R2). 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.<\/jats:p>","DOI":"10.3390\/w13233379","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T03:12:56Z","timestamp":1638328376000},"page":"3379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2650-8123","authenticated-orcid":false,"given":"Rana Muhammad","family":"Adnan","sequence":"first","affiliation":[{"name":"School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China"},{"name":"State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6917-7873","authenticated-orcid":false,"given":"Reham R.","family":"Mostafa","sequence":"additional","affiliation":[{"name":"Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5779-1382","authenticated-orcid":false,"given":"Abu Reza Md. Towfiqul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh"}]},{"given":"Alireza Docheshmeh","family":"Gorgij","sequence":"additional","affiliation":[{"name":"Faculty of Industry and Mining (Khash), University of Sistan and Baluchestan, Zahedan 9816745845, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7464-8377","authenticated-orcid":false,"given":"Alban","family":"Kuriqi","sequence":"additional","affiliation":[{"name":"CERIS, Instituto Superior Tecnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7847-5872","authenticated-orcid":false,"given":"Ozgur","family":"Kisi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, University of Applied Sciences, 23562 L\u00fcbeck, Germany"},{"name":"Department of Civil Engineering, Ilia State University, Tbilisi 0162, Georgia"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s00477-012-0589-6","article-title":"Analysis of dry\/wet conditions using the standardized precipitation index and its potential usefulness for drought\/flood monitoring in Hunan Province, China","volume":"27","author":"Du","year":"2013","journal-title":"Stoch. 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