{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T10:08:14Z","timestamp":1776679694446,"version":"3.51.2"},"reference-count":84,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T00:00:00Z","timestamp":1600214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algorithms for merging multiple global precipitation datasets to improve the spatiotemporal rainfall characterization. SPEM2L is applied over the Krishna River Basin (KRB), India for 34 years spanning from 1985 to 2018, using daily measurements from three Secondary Precipitation Products (SPPs). Sixteen Machine Learning Algorithms (MLAs) were applied on three SPPs under four combinations to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. Multilayer Perceptron Neural Network with Bayesian Regularization (NBR) algorithm employing three SPPs integration outperformed all other Machine Learning Models (MLMs) and two dataset integration combinations. The merged NBR product exhibited improvements in terms of continuous and categorical statistics at all temporal scales as well as in all climatic zones. Our results indicate that the SPEM2L procedure could be successfully used in any other region or basin that has a poor gauging network or where a single precipitation product performance is ineffective.<\/jats:p>","DOI":"10.3390\/rs12183013","type":"journal-article","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T10:30:12Z","timestamp":1600252212000},"page":"3013","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Secondary Precipitation Estimate Merging Using Machine Learning: Development and Evaluation over Krishna River Basin, India"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2110-5560","authenticated-orcid":false,"given":"Venkatesh","family":"Kolluru","sequence":"first","affiliation":[{"name":"Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Maharashtra 400076, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1619-1353","authenticated-orcid":false,"given":"Srinivas","family":"Kolluru","sequence":"additional","affiliation":[{"name":"Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Maharashtra 400076, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5921-0382","authenticated-orcid":false,"given":"Nimisha","family":"Wagle","sequence":"additional","affiliation":[{"name":"Survey Department, Government of Nepal, Minbhawan, Kathmandu 44600, Nepal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0886-4201","authenticated-orcid":false,"given":"Tri Dev","family":"Acharya","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Kangwon National University, Chuncheon 24341, Korea"},{"name":"Institute of Industrial Technology, Kangwon National University, Chuncheon 24341, Korea"},{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China"},{"name":"Institute of Transportation Studies, University of California Davis, Davis, CA 95616, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.jhydrol.2013.06.039","article-title":"Merging gauge and satellite rainfall with specification of associated uncertainty across Australia","volume":"499","author":"Woldemeskel","year":"2013","journal-title":"J. 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