{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T14:55:32Z","timestamp":1773413732805,"version":"3.50.1"},"reference-count":52,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:00:00Z","timestamp":1611014400000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009694","name":"Bu-Ali Sina University","doi-asserted-by":"publisher","award":["98-941"],"award-info":[{"award-number":["98-941"]}],"id":[{"id":"10.13039\/501100009694","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Precipitation deficit causes meteorological drought, and its continuation appears as other different types of droughts including hydrological, agricultural, economic, and social droughts. Multivariate Standardized Precipitation Index (MSPI) can show the drought status from the perspective of different drought types simultaneously. Forecasting multivariate droughts can provide good information about the future status of a region and will be applicable for the planners of different water divisions. In this study, the MLP model and its hybrid form with the Imperialistic Competitive Algorithm (MLP\u2010ICA) have been investigated for the first time in multivariate drought studies. For this purpose, two semi\u2010arid stations of western Iran were selected, and their precipitation data were provided from the Iranian Meteorological Organization (IRIMO), during the period of 1988\u20132017. MSPI was calculated in 5\u2010time windows of the multivariate drought, including MSPI<jats:sub>3\u20136<\/jats:sub> (drought in perspectives of soil moisture and surface hydrology simultaneously), MSPI<jats:sub>6\u201312<\/jats:sub> (hydrological and agricultural droughts simultaneously), MSPI<jats:sub>3\u201312<\/jats:sub> (soil moisture, surface hydrology, and agricultural droughts simultaneously), MSPI<jats:sub>12\u201324<\/jats:sub> (drought in perspectives of agriculture and groundwater simultaneously), and MSPI<jats:sub>24\u201348<\/jats:sub> (socio\u2010economical droughts). The results showed acceptable performances in forecasting multivariate droughts. In both stations, the larger time windows (MSPI<jats:sub>12\u201324<\/jats:sub> and MSPI<jats:sub>24\u201348<\/jats:sub>) had better predictions than the smaller ones (MSPI<jats:sub>3\u20136<\/jats:sub>, MSPI<jats:sub>6\u201312<\/jats:sub>, and MSPI<jats:sub>3\u201312<\/jats:sub>). Generally, it can be reported that, by decreasing the size of the time window, the gradual changes of the index give way to sudden jumps. This causes weaker autocorrelation and consequently weaker predictions, e.g., forecasting droughts from the perspective of soil moisture and surface hydrology simultaneously (MSPI<jats:sub>3\u20136<\/jats:sub>). The hybrid MLP\u2010ICA shows stronger prediction results than the simple MLP model in all comparisons. The ICA optimizer could averagely improve MLP\u2019s accuracy by 28.5%, which is a significant improvement. According to the evaluations (RMSE\u2009=\u20090.20; MAE\u2009=\u20090.15; <jats:italic>R<\/jats:italic>\u2009=\u20090.95), the results are hopeful for simultaneous forecasting of different drought types and can be tested for other similar areas.<\/jats:p>","DOI":"10.1155\/2021\/6610228","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T03:35:05Z","timestamp":1611113705000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Forecasting Different Types of Droughts Simultaneously Using Multivariate Standardized Precipitation Index (MSPI), MLP Neural Network, and Imperialistic Competitive Algorithm (ICA)"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5640-865X","authenticated-orcid":false,"given":"Pouya","family":"Aghelpour","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9705-3066","authenticated-orcid":false,"given":"Vahid","family":"Varshavian","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,1,19]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-014-0533-2"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-3224-8_6"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2010.07.012"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosres.2020.105007"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2019.06.010"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-015-0926-x"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/00207230500288968"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1175\/bams-d-11-00176.1"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.wace.2014.03.005"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/w8020043"},{"key":"e_1_2_9_11_2","article-title":"Drought index prediction using data intelligent analytic models: a review","author":"Yaseen Z. M.","year":"2020","journal-title":"Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105279"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.124053"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-019-02825-9"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11027-013-9464-0"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40899-015-0040-5"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1002\/joc.2215"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.2166\/wcc.2019.236"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs12203437"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2020.106145"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108127"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)ir.1943-4774.0001471"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00477-019-01761-4"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-019-02905-w"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2017.09.078"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-020-05437-0"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1080\/02626667.2019.1676428"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00024-020-02570-5"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1080\/09715010.2018.1498754"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11600-020-00419-y"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-020-5239-6"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-020-04180-9"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.advwatres.2020.103562"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/794061"},{"key":"e_1_2_9_35_2","doi-asserted-by":"crossref","unstructured":"HongD.andHongK. A. Drought forecasting using MLP neural networks Proceedings of the 2015 8th International Conference on U-And E-Service Science and Technology (UNESST) November 2015 Jeju South Korea IEEE 62\u201365 https:\/\/doi.org\/10.1109\/UNESST.2015.23 2-s2.0-84965141360.","DOI":"10.1109\/UNESST.2015.23"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/3868519"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-016-2750-x"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00477-020-01949-z"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi9120701"},{"key":"e_1_2_9_40_2","volume-title":"Standardized Precipitation Index User Guide","author":"Svoboda M.","year":"2012"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2009.10.029"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)he.1943-5584.0001654"},{"key":"e_1_2_9_43_2","doi-asserted-by":"crossref","unstructured":"Atashpaz-GargariE.andLucasC. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition Proceedings of the 2007 IEEE Congress on Evolutionary Computation September 2017 Singapore 4661\u20134667 https:\/\/doi.org\/10.1109\/CEC.2007.4425083 2-s2.0-78149290542.","DOI":"10.1109\/CEC.2007.4425083"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-012-0741-8"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1029\/2003wr002610"},{"key":"e_1_2_9_46_2","first-page":"179","article-title":"The relationship of drought frequency and duration to time scales","volume":"17","author":"McKee T. B.","year":"1993","journal-title":"Applied Climatology"},{"key":"e_1_2_9_47_2","volume-title":"Handbook of Drought Indicators and Indices","author":"Svoboda M.","year":"2016"},{"key":"e_1_2_9_48_2","volume-title":"Neural Networks: A Comprehensive Foundation","author":"Haykin S.","year":"2007"},{"key":"e_1_2_9_49_2","doi-asserted-by":"publisher","DOI":"10.21236\/ADA164453"},{"key":"e_1_2_9_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40808-017-0385-x"},{"key":"e_1_2_9_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-2024-7"},{"key":"e_1_2_9_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dynatmoce.2018.12.001"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/6610228.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/6610228.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/6610228","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T22:12:05Z","timestamp":1723241525000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/6610228"}},"subtitle":[],"editor":[{"given":"Shamsuddin","family":"Shahid","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/6610228"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6610228","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2020-10-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-12-29","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-01-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6610228"}}