{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:51:07Z","timestamp":1765486267245,"version":"3.41.2"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2019,1,2]],"date-time":"2019-01-02T00:00:00Z","timestamp":1546387200000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["RG-1437-027"],"award-info":[{"award-number":["RG-1437-027"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed component prediction and summation. The proposed architecture is applied on daily rivers inflow time series data of Indus Basin System. The performances of the proposed models are compared with traditional single\u2010stage model (without denoised and decomposed), the hybrid two\u2010stage model (with denoised), and existing three\u2010stage hybrid model (with denoised and decomposition). Three evaluation measures are used to assess the prediction accuracy of all models such as Mean Relative Error (MRE), Mean Absolute Error (MAE), and Mean Square Error (MSE). The proposed, three\u2010stage hybrid models have shown improvement in prediction accuracy with minimum MRE, MAE, and MSE for all case studies as compared to other existing one\u2010stage and two\u2010stage models. In summary, the accuracy of prediction is improved by reducing the complexity of hydrological time series data by incorporating the denoising and decomposition.<\/jats:p>","DOI":"10.1155\/2019\/2782715","type":"journal-article","created":{"date-parts":[[2019,1,2]],"date-time":"2019-01-02T13:08:01Z","timestamp":1546434481000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0998-6369","authenticated-orcid":false,"given":"Hafiza Mamona","family":"Nazir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1586-1503","authenticated-orcid":false,"given":"Ijaz","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4885-4251","authenticated-orcid":false,"given":"Muhammad","family":"Faisal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alaa Mohamd","family":"Shoukry","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Showkat","family":"Gani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1124-7485","authenticated-orcid":false,"given":"Ishfaq","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,1,2]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.2166\/hydro.2008.015"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0104663"},{"key":"e_1_2_9_3_2","unstructured":"IslamZ. 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