Loading…

Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil

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...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-06, Vol.16 (11), p.1971
Main Authors: Verdelho, Fernanda F., Beneti, Cesar, Pavam, Luis G., Calvetti, Leonardo, Oliveira, Luiz E. S., Zanata Alves, Marco A.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c359t-fc985f6f9bdbfab25605273f7eb99f8132c836bf8c2498408d7c1a70a94f2e523
container_end_page
container_issue 11
container_start_page 1971
container_title Remote sensing (Basel, Switzerland)
container_volume 16
creator Verdelho, Fernanda F.
Beneti, Cesar
Pavam, Luis G.
Calvetti, Leonardo
Oliveira, Luiz E. S.
Zanata Alves, Marco A.
description 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–R 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–R relationship. The results highlight machine learning’s 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.
doi_str_mv 10.3390/rs16111971
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_b623c6a7561c4045b7bb6334de699c1d</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A797902971</galeid><doaj_id>oai_doaj_org_article_b623c6a7561c4045b7bb6334de699c1d</doaj_id><sourcerecordid>A797902971</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-fc985f6f9bdbfab25605273f7eb99f8132c836bf8c2498408d7c1a70a94f2e523</originalsourceid><addsrcrecordid>eNpNkU9vEzEQxVcIJKrSC5_AEjektP639voYSguVgiiFiqM19tobR4kdbAepnPjoOF0EtQ8ej977aUav614TfM6Ywhe5EEEIUZI8604olnTBqaLPn9Qvu7NSNrgdxojC_KT7_eUAsYYKNfx06DY7G_aPvxTRValhN5f3JcQJfXdQ1y6jOxgho_dQAUEc0Sew6xAdWjnI8ahbbqeUQ13vCvIpo-ZBX9PhaI3ozk1HYPLoXYZfYfuqe-FhW9zZ3_e0u7---nb5cbH6_OHmcrlaWNaruvBWDb0XXpnReDC0F7inknnpjFJ-IIzagQnjB0u5GjgeRmkJSAyKe-p6yk67m5k7JtjofW6b5QedIOjHRsqThlyD3TptBGVWgOwFsRzz3khjBGN8dEIpS8bGejOz9jn9OLhS9SYdcmzja4aF5ExgKZvqfFZN0KAh-lQz2HZHtws2RedD6y-lkgrTFlozvJ0NNqdSsvP_xiRYHxPW_xNmfwDxdZiU</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3067436077</pqid></control><display><type>article</type><title>Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil</title><source>Publicly Available Content (ProQuest)</source><creator>Verdelho, Fernanda F. ; Beneti, Cesar ; Pavam, Luis G. ; Calvetti, Leonardo ; Oliveira, Luiz E. S. ; Zanata Alves, Marco A.</creator><creatorcontrib>Verdelho, Fernanda F. ; Beneti, Cesar ; Pavam, Luis G. ; Calvetti, Leonardo ; Oliveira, Luiz E. S. ; Zanata Alves, Marco A.</creatorcontrib><description>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–R 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–R relationship. The results highlight machine learning’s 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.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs16111971</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Artificial intelligence ; Calibration ; Comparative analysis ; Data collection ; Datasets ; Environmental aspects ; Flash floods ; Flood forecasting ; Flood management ; Gauges ; gradient boosting ; Hydroelectric power ; Hydroelectric power generation ; Learning algorithms ; Machine learning ; Measurement ; Meteorological radar ; Precipitation ; Precipitation (Meteorology) ; precipitation estimation ; quantitative precipitation estimation ; Radar ; Radar data ; Radar systems ; Rain ; Rainfall ; random forest ; Research methodology ; Variables ; Weather ; Weather forecasting</subject><ispartof>Remote sensing (Basel, Switzerland), 2024-06, Vol.16 (11), p.1971</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c359t-fc985f6f9bdbfab25605273f7eb99f8132c836bf8c2498408d7c1a70a94f2e523</cites><orcidid>0000-0002-0620-5504 ; 0000-0003-2440-2664 ; 0000-0002-0595-5370 ; 0009-0003-7475-9044 ; 0000-0002-7989-0282 ; 0000-0002-0635-4710</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3067436077/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3067436077?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,25731,27901,27902,36989,44566,74869</link.rule.ids></links><search><creatorcontrib>Verdelho, Fernanda F.</creatorcontrib><creatorcontrib>Beneti, Cesar</creatorcontrib><creatorcontrib>Pavam, Luis G.</creatorcontrib><creatorcontrib>Calvetti, Leonardo</creatorcontrib><creatorcontrib>Oliveira, Luiz E. S.</creatorcontrib><creatorcontrib>Zanata Alves, Marco A.</creatorcontrib><title>Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil</title><title>Remote sensing (Basel, Switzerland)</title><description>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–R 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–R relationship. The results highlight machine learning’s 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.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Calibration</subject><subject>Comparative analysis</subject><subject>Data collection</subject><subject>Datasets</subject><subject>Environmental aspects</subject><subject>Flash floods</subject><subject>Flood forecasting</subject><subject>Flood management</subject><subject>Gauges</subject><subject>gradient boosting</subject><subject>Hydroelectric power</subject><subject>Hydroelectric power generation</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Measurement</subject><subject>Meteorological radar</subject><subject>Precipitation</subject><subject>Precipitation (Meteorology)</subject><subject>precipitation estimation</subject><subject>quantitative precipitation estimation</subject><subject>Radar</subject><subject>Radar data</subject><subject>Radar systems</subject><subject>Rain</subject><subject>Rainfall</subject><subject>random forest</subject><subject>Research methodology</subject><subject>Variables</subject><subject>Weather</subject><subject>Weather forecasting</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkU9vEzEQxVcIJKrSC5_AEjektP639voYSguVgiiFiqM19tobR4kdbAepnPjoOF0EtQ8ej977aUav614TfM6Ywhe5EEEIUZI8604olnTBqaLPn9Qvu7NSNrgdxojC_KT7_eUAsYYKNfx06DY7G_aPvxTRValhN5f3JcQJfXdQ1y6jOxgho_dQAUEc0Sew6xAdWjnI8ahbbqeUQ13vCvIpo-ZBX9PhaI3ozk1HYPLoXYZfYfuqe-FhW9zZ3_e0u7---nb5cbH6_OHmcrlaWNaruvBWDb0XXpnReDC0F7inknnpjFJ-IIzagQnjB0u5GjgeRmkJSAyKe-p6yk67m5k7JtjofW6b5QedIOjHRsqThlyD3TptBGVWgOwFsRzz3khjBGN8dEIpS8bGejOz9jn9OLhS9SYdcmzja4aF5ExgKZvqfFZN0KAh-lQz2HZHtws2RedD6y-lkgrTFlozvJ0NNqdSsvP_xiRYHxPW_xNmfwDxdZiU</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Verdelho, Fernanda F.</creator><creator>Beneti, Cesar</creator><creator>Pavam, Luis G.</creator><creator>Calvetti, Leonardo</creator><creator>Oliveira, Luiz E. S.</creator><creator>Zanata Alves, Marco A.</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0620-5504</orcidid><orcidid>https://orcid.org/0000-0003-2440-2664</orcidid><orcidid>https://orcid.org/0000-0002-0595-5370</orcidid><orcidid>https://orcid.org/0009-0003-7475-9044</orcidid><orcidid>https://orcid.org/0000-0002-7989-0282</orcidid><orcidid>https://orcid.org/0000-0002-0635-4710</orcidid></search><sort><creationdate>20240601</creationdate><title>Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil</title><author>Verdelho, Fernanda F. ; Beneti, Cesar ; Pavam, Luis G. ; Calvetti, Leonardo ; Oliveira, Luiz E. S. ; Zanata Alves, Marco A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-fc985f6f9bdbfab25605273f7eb99f8132c836bf8c2498408d7c1a70a94f2e523</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Calibration</topic><topic>Comparative analysis</topic><topic>Data collection</topic><topic>Datasets</topic><topic>Environmental aspects</topic><topic>Flash floods</topic><topic>Flood forecasting</topic><topic>Flood management</topic><topic>Gauges</topic><topic>gradient boosting</topic><topic>Hydroelectric power</topic><topic>Hydroelectric power generation</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Measurement</topic><topic>Meteorological radar</topic><topic>Precipitation</topic><topic>Precipitation (Meteorology)</topic><topic>precipitation estimation</topic><topic>quantitative precipitation estimation</topic><topic>Radar</topic><topic>Radar data</topic><topic>Radar systems</topic><topic>Rain</topic><topic>Rainfall</topic><topic>random forest</topic><topic>Research methodology</topic><topic>Variables</topic><topic>Weather</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Verdelho, Fernanda F.</creatorcontrib><creatorcontrib>Beneti, Cesar</creatorcontrib><creatorcontrib>Pavam, Luis G.</creatorcontrib><creatorcontrib>Calvetti, Leonardo</creatorcontrib><creatorcontrib>Oliveira, Luiz E. S.</creatorcontrib><creatorcontrib>Zanata Alves, Marco A.</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Remote sensing (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Verdelho, Fernanda F.</au><au>Beneti, Cesar</au><au>Pavam, Luis G.</au><au>Calvetti, Leonardo</au><au>Oliveira, Luiz E. S.</au><au>Zanata Alves, Marco A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2024-06-01</date><risdate>2024</risdate><volume>16</volume><issue>11</issue><spage>1971</spage><pages>1971-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>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–R 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–R relationship. The results highlight machine learning’s 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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs16111971</doi><orcidid>https://orcid.org/0000-0002-0620-5504</orcidid><orcidid>https://orcid.org/0000-0003-2440-2664</orcidid><orcidid>https://orcid.org/0000-0002-0595-5370</orcidid><orcidid>https://orcid.org/0009-0003-7475-9044</orcidid><orcidid>https://orcid.org/0000-0002-7989-0282</orcidid><orcidid>https://orcid.org/0000-0002-0635-4710</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2072-4292
ispartof Remote sensing (Basel, Switzerland), 2024-06, Vol.16 (11), p.1971
issn 2072-4292
2072-4292
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_b623c6a7561c4045b7bb6334de699c1d
source Publicly Available Content (ProQuest)
subjects Algorithms
Artificial intelligence
Calibration
Comparative analysis
Data collection
Datasets
Environmental aspects
Flash floods
Flood forecasting
Flood management
Gauges
gradient boosting
Hydroelectric power
Hydroelectric power generation
Learning algorithms
Machine learning
Measurement
Meteorological radar
Precipitation
Precipitation (Meteorology)
precipitation estimation
quantitative precipitation estimation
Radar
Radar data
Radar systems
Rain
Rainfall
random forest
Research methodology
Variables
Weather
Weather forecasting
title Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T06%3A24%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Quantitative%20Precipitation%20Estimation%20Using%20Weather%20Radar%20Data%20and%20Machine%20Learning%20Algorithms%20for%20the%20Southern%20Region%20of%20Brazil&rft.jtitle=Remote%20sensing%20(Basel,%20Switzerland)&rft.au=Verdelho,%20Fernanda%20F.&rft.date=2024-06-01&rft.volume=16&rft.issue=11&rft.spage=1971&rft.pages=1971-&rft.issn=2072-4292&rft.eissn=2072-4292&rft_id=info:doi/10.3390/rs16111971&rft_dat=%3Cgale_doaj_%3EA797902971%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c359t-fc985f6f9bdbfab25605273f7eb99f8132c836bf8c2498408d7c1a70a94f2e523%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3067436077&rft_id=info:pmid/&rft_galeid=A797902971&rfr_iscdi=true