{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:51:45Z","timestamp":1773157905329,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"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>In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z\u2013R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z\u2013R relationship. The results highlight machine learning\u2019s potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method.<\/jats:p>","DOI":"10.3390\/rs16111971","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T10:42:34Z","timestamp":1717065754000},"page":"1971","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7989-0282","authenticated-orcid":false,"given":"Fernanda F.","family":"Verdelho","sequence":"first","affiliation":[{"name":"Parana Environmental Technology and Monitoring System\u2014SIMEPAR, Curitiba 81530-900, Brazil"},{"name":"Department of Computer Science, Polytechnic Center, Federal University of Paran\u00e1\u2014UFPR, Curitiba 81530-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0635-4710","authenticated-orcid":false,"given":"Cesar","family":"Beneti","sequence":"additional","affiliation":[{"name":"Parana Environmental Technology and Monitoring System\u2014SIMEPAR, Curitiba 81530-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7475-9044","authenticated-orcid":false,"suffix":"Jr.","given":"Luis G.","family":"Pavam","sequence":"additional","affiliation":[{"name":"Parana Environmental Technology and Monitoring System\u2014SIMEPAR, Curitiba 81530-900, Brazil"},{"name":"Department of Computer Science, Polytechnic Center, Federal University of Paran\u00e1\u2014UFPR, Curitiba 81530-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0620-5504","authenticated-orcid":false,"given":"Leonardo","family":"Calvetti","sequence":"additional","affiliation":[{"name":"Department of Meteorology, Federal University of Pelotas\u2014UFPEL, Pelotas 96010-610, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0595-5370","authenticated-orcid":false,"given":"Luiz E. S.","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Polytechnic Center, Federal University of Paran\u00e1\u2014UFPR, Curitiba 81530-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2440-2664","authenticated-orcid":false,"given":"Marco A.","family":"Zanata Alves","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Polytechnic Center, Federal University of Paran\u00e1\u2014UFPR, Curitiba 81530-000, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"ref_1","unstructured":"ANEEL (2022, February 23). Ag\u00eancia Nacional de Energia El\u00e9trica\u2014Gera\u00e7\u00e3o, Available online: https:\/\/www.aneel.gov.br\/."},{"key":"ref_2","unstructured":"IDR-Paran\u00e1 (2022, February 23). 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