Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam
Seasonal rainfall forecasting is important for water resources management, agriculture, and disaster prevention. Our study aims to provide an automated deep learning method for the seasonal prediction of monthly rainfall at stations in seven climatic sub-regions in Vietnam with lead times of up to 6...
Gespeichert in:
| Veröffentlicht in: | Earth science informatics 2024-10, Vol.17 (5), p.3925-3944 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3944 |
|---|---|
| container_issue | 5 |
| container_start_page | 3925 |
| container_title | Earth science informatics |
| container_volume | 17 |
| creator | Nguyen-Duc, Phu Nguyen, Huu Duy Nguyen, Quoc-Huy Phan-Van, Tan Pham-Thanh, Ha |
| description | Seasonal rainfall forecasting is important for water resources management, agriculture, and disaster prevention. Our study aims to provide an automated deep learning method for the seasonal prediction of monthly rainfall at stations in seven climatic sub-regions in Vietnam with lead times of up to 6 months. An appropriate set of predictors was selected based on numerous climate indices and neighbor station data for the period 1980–2020. We developed an adapted deep learning pipeline for both short- and long-term analysis. The predicted rainfall was verified against the observed data using mean absolute error (MAE), root mean squared error (RMSE), and Pearson correlation coefficients. The results showed that our model generally captured well observed data reflected by low error (MAE and RMSE |
| doi_str_mv | 10.1007/s12145-024-01414-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3117181728</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3117181728</sourcerecordid><originalsourceid>FETCH-LOGICAL-c200t-76ff69a1d80c533d1d81bcf45b6c9c3dfa5c16762f2c3f564d481946e85545743</originalsourceid><addsrcrecordid>eNp9UMlOwzAUjBBIVNAf4GSJCxwCft6SHquKTUrh0MLVch27DSRxsFOh_j2mYblxeqPRzGjeJMkZ4CvAOLsOQIDxFBOWYmDAUnqQjCAXkWI5HP7ijB4n4xCqFaZABCUkHyVu2nV1pVVfuRY5iwrXrtFi43yfLo1v0Nw0zu_QRbFYzi_Ro-k_nH9D1nkUjAquVTXqvCkr_RPQuLbf1DvkVdVaVddIae9CQC-V6VvVnCZHkQ1m_H1Pkufbm-XsPi2e7h5m0yLVBOM-zYS1YqKgzLHmlJYRwEpbxldCTzQtreIaRCaIJZpaLlgZP50wYXLOGc8YPUnOh9zOu_etCb18dVsf6wZJATLIISN5VJFBte_ojZWdrxrldxKw_NpWDtvKuK3cbytpNNHBFKK4XRv_F_2P6xM9xHwb</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3117181728</pqid></control><display><type>article</type><title>Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam</title><source>SpringerLink Journals</source><creator>Nguyen-Duc, Phu ; Nguyen, Huu Duy ; Nguyen, Quoc-Huy ; Phan-Van, Tan ; Pham-Thanh, Ha</creator><creatorcontrib>Nguyen-Duc, Phu ; Nguyen, Huu Duy ; Nguyen, Quoc-Huy ; Phan-Van, Tan ; Pham-Thanh, Ha</creatorcontrib><description>Seasonal rainfall forecasting is important for water resources management, agriculture, and disaster prevention. Our study aims to provide an automated deep learning method for the seasonal prediction of monthly rainfall at stations in seven climatic sub-regions in Vietnam with lead times of up to 6 months. An appropriate set of predictors was selected based on numerous climate indices and neighbor station data for the period 1980–2020. We developed an adapted deep learning pipeline for both short- and long-term analysis. The predicted rainfall was verified against the observed data using mean absolute error (MAE), root mean squared error (RMSE), and Pearson correlation coefficients. The results showed that our model generally captured well observed data reflected by low error (MAE and RMSE < 0.2) and high correlation (at 0.8–0.9) for all climatic sub-regions. For the leadtimes of 1–3 months, the rainfall predictionsmade using climate indices as predictors were outperformed by those using neighbor stations data; while for longer leadtimes (4–6 months), the climate indices themselve were able to improve the performance. The rainfall predictions of our methods on all three lead times climatological predictions by factoring additional values. However, there is room for improvement in predicting extreme and abrupt shifts in time series patterns.</description><identifier>ISSN: 1865-0473</identifier><identifier>EISSN: 1865-0481</identifier><identifier>DOI: 10.1007/s12145-024-01414-3</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Climatic indexes ; Correlation coefficient ; Correlation coefficients ; Deep learning ; Earth and Environmental Science ; Earth Sciences ; Earth System Sciences ; Emergency preparedness ; Error analysis ; Extreme values ; Information Systems Applications (incl.Internet) ; Monthly rainfall ; Ontology ; Performance prediction ; Rainfall ; Rainfall forecasting ; Review ; Root-mean-square errors ; Seasonal forecasting ; Seasonal rainfall ; Simulation and Modeling ; Space Exploration and Astronautics ; Space Sciences (including Extraterrestrial Physics ; Water resources management</subject><ispartof>Earth science informatics, 2024-10, Vol.17 (5), p.3925-3944</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c200t-76ff69a1d80c533d1d81bcf45b6c9c3dfa5c16762f2c3f564d481946e85545743</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12145-024-01414-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12145-024-01414-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Nguyen-Duc, Phu</creatorcontrib><creatorcontrib>Nguyen, Huu Duy</creatorcontrib><creatorcontrib>Nguyen, Quoc-Huy</creatorcontrib><creatorcontrib>Phan-Van, Tan</creatorcontrib><creatorcontrib>Pham-Thanh, Ha</creatorcontrib><title>Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam</title><title>Earth science informatics</title><addtitle>Earth Sci Inform</addtitle><description>Seasonal rainfall forecasting is important for water resources management, agriculture, and disaster prevention. Our study aims to provide an automated deep learning method for the seasonal prediction of monthly rainfall at stations in seven climatic sub-regions in Vietnam with lead times of up to 6 months. An appropriate set of predictors was selected based on numerous climate indices and neighbor station data for the period 1980–2020. We developed an adapted deep learning pipeline for both short- and long-term analysis. The predicted rainfall was verified against the observed data using mean absolute error (MAE), root mean squared error (RMSE), and Pearson correlation coefficients. The results showed that our model generally captured well observed data reflected by low error (MAE and RMSE < 0.2) and high correlation (at 0.8–0.9) for all climatic sub-regions. For the leadtimes of 1–3 months, the rainfall predictionsmade using climate indices as predictors were outperformed by those using neighbor stations data; while for longer leadtimes (4–6 months), the climate indices themselve were able to improve the performance. The rainfall predictions of our methods on all three lead times climatological predictions by factoring additional values. However, there is room for improvement in predicting extreme and abrupt shifts in time series patterns.</description><subject>Climatic indexes</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Deep learning</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earth System Sciences</subject><subject>Emergency preparedness</subject><subject>Error analysis</subject><subject>Extreme values</subject><subject>Information Systems Applications (incl.Internet)</subject><subject>Monthly rainfall</subject><subject>Ontology</subject><subject>Performance prediction</subject><subject>Rainfall</subject><subject>Rainfall forecasting</subject><subject>Review</subject><subject>Root-mean-square errors</subject><subject>Seasonal forecasting</subject><subject>Seasonal rainfall</subject><subject>Simulation and Modeling</subject><subject>Space Exploration and Astronautics</subject><subject>Space Sciences (including Extraterrestrial Physics</subject><subject>Water resources management</subject><issn>1865-0473</issn><issn>1865-0481</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9UMlOwzAUjBBIVNAf4GSJCxwCft6SHquKTUrh0MLVch27DSRxsFOh_j2mYblxeqPRzGjeJMkZ4CvAOLsOQIDxFBOWYmDAUnqQjCAXkWI5HP7ijB4n4xCqFaZABCUkHyVu2nV1pVVfuRY5iwrXrtFi43yfLo1v0Nw0zu_QRbFYzi_Ro-k_nH9D1nkUjAquVTXqvCkr_RPQuLbf1DvkVdVaVddIae9CQC-V6VvVnCZHkQ1m_H1Pkufbm-XsPi2e7h5m0yLVBOM-zYS1YqKgzLHmlJYRwEpbxldCTzQtreIaRCaIJZpaLlgZP50wYXLOGc8YPUnOh9zOu_etCb18dVsf6wZJATLIISN5VJFBte_ojZWdrxrldxKw_NpWDtvKuK3cbytpNNHBFKK4XRv_F_2P6xM9xHwb</recordid><startdate>20241001</startdate><enddate>20241001</enddate><creator>Nguyen-Duc, Phu</creator><creator>Nguyen, Huu Duy</creator><creator>Nguyen, Quoc-Huy</creator><creator>Phan-Van, Tan</creator><creator>Pham-Thanh, Ha</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TG</scope><scope>8FD</scope><scope>JQ2</scope><scope>KL.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20241001</creationdate><title>Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam</title><author>Nguyen-Duc, Phu ; Nguyen, Huu Duy ; Nguyen, Quoc-Huy ; Phan-Van, Tan ; Pham-Thanh, Ha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c200t-76ff69a1d80c533d1d81bcf45b6c9c3dfa5c16762f2c3f564d481946e85545743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Climatic indexes</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Deep learning</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Earth System Sciences</topic><topic>Emergency preparedness</topic><topic>Error analysis</topic><topic>Extreme values</topic><topic>Information Systems Applications (incl.Internet)</topic><topic>Monthly rainfall</topic><topic>Ontology</topic><topic>Performance prediction</topic><topic>Rainfall</topic><topic>Rainfall forecasting</topic><topic>Review</topic><topic>Root-mean-square errors</topic><topic>Seasonal forecasting</topic><topic>Seasonal rainfall</topic><topic>Simulation and Modeling</topic><topic>Space Exploration and Astronautics</topic><topic>Space Sciences (including Extraterrestrial Physics</topic><topic>Water resources management</topic><toplevel>online_resources</toplevel><creatorcontrib>Nguyen-Duc, Phu</creatorcontrib><creatorcontrib>Nguyen, Huu Duy</creatorcontrib><creatorcontrib>Nguyen, Quoc-Huy</creatorcontrib><creatorcontrib>Phan-Van, Tan</creatorcontrib><creatorcontrib>Pham-Thanh, Ha</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Earth science informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nguyen-Duc, Phu</au><au>Nguyen, Huu Duy</au><au>Nguyen, Quoc-Huy</au><au>Phan-Van, Tan</au><au>Pham-Thanh, Ha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam</atitle><jtitle>Earth science informatics</jtitle><stitle>Earth Sci Inform</stitle><date>2024-10-01</date><risdate>2024</risdate><volume>17</volume><issue>5</issue><spage>3925</spage><epage>3944</epage><pages>3925-3944</pages><issn>1865-0473</issn><eissn>1865-0481</eissn><abstract>Seasonal rainfall forecasting is important for water resources management, agriculture, and disaster prevention. Our study aims to provide an automated deep learning method for the seasonal prediction of monthly rainfall at stations in seven climatic sub-regions in Vietnam with lead times of up to 6 months. An appropriate set of predictors was selected based on numerous climate indices and neighbor station data for the period 1980–2020. We developed an adapted deep learning pipeline for both short- and long-term analysis. The predicted rainfall was verified against the observed data using mean absolute error (MAE), root mean squared error (RMSE), and Pearson correlation coefficients. The results showed that our model generally captured well observed data reflected by low error (MAE and RMSE < 0.2) and high correlation (at 0.8–0.9) for all climatic sub-regions. For the leadtimes of 1–3 months, the rainfall predictionsmade using climate indices as predictors were outperformed by those using neighbor stations data; while for longer leadtimes (4–6 months), the climate indices themselve were able to improve the performance. The rainfall predictions of our methods on all three lead times climatological predictions by factoring additional values. However, there is room for improvement in predicting extreme and abrupt shifts in time series patterns.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12145-024-01414-3</doi><tpages>20</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1865-0473 |
| ispartof | Earth science informatics, 2024-10, Vol.17 (5), p.3925-3944 |
| issn | 1865-0473 1865-0481 |
| language | eng |
| recordid | cdi_proquest_journals_3117181728 |
| source | SpringerLink Journals |
| subjects | Climatic indexes Correlation coefficient Correlation coefficients Deep learning Earth and Environmental Science Earth Sciences Earth System Sciences Emergency preparedness Error analysis Extreme values Information Systems Applications (incl.Internet) Monthly rainfall Ontology Performance prediction Rainfall Rainfall forecasting Review Root-mean-square errors Seasonal forecasting Seasonal rainfall Simulation and Modeling Space Exploration and Astronautics Space Sciences (including Extraterrestrial Physics Water resources management |
| title | Application of Long Short-Term Memory (LSTM) Network for seasonal prediction of monthly rainfall across Vietnam |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T11%3A37%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Application%20of%20Long%20Short-Term%20Memory%20(LSTM)%20Network%20for%20seasonal%20prediction%20of%20monthly%20rainfall%20across%20Vietnam&rft.jtitle=Earth%20science%20informatics&rft.au=Nguyen-Duc,%20Phu&rft.date=2024-10-01&rft.volume=17&rft.issue=5&rft.spage=3925&rft.epage=3944&rft.pages=3925-3944&rft.issn=1865-0473&rft.eissn=1865-0481&rft_id=info:doi/10.1007/s12145-024-01414-3&rft_dat=%3Cproquest_cross%3E3117181728%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3117181728&rft_id=info:pmid/&rfr_iscdi=true |