Intelligent long-term runoff forecasting method of machine learning model based on grid units
The invention discloses an intelligent long-term runoff forecasting method of a machine learning model based on grid units. The intelligent long-term runoff forecasting method takes the theory and method of machine learning as the basis of modeling. Comprising the steps of raster data processing, fa...
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | CHU HAIBO WANG ZHUOQI WANG ZONGHAN |
description | The invention discloses an intelligent long-term runoff forecasting method of a machine learning model based on grid units. The intelligent long-term runoff forecasting method takes the theory and method of machine learning as the basis of modeling. Comprising the steps of raster data processing, factor hysteresis analysis, machine learning model modeling, parameter optimization and model verification. The corresponding method comprises the steps of grid conversion, utilization of a time delay cross-correlation method, a machine learning model and a verification method. According to the medium-and-long-term runoff forecasting method provided by the invention, the determination coefficients are used for evaluation, and the determination coefficient values can reach 0.8 or above and reach the second-class precision or above of the hydrological information forecasting specification (GB/T222482-2008).
本发明公开了一种基于栅格单元的机器学习模型的智能长期径流预报方法,本发明以机器学习的理论和方法作为建模的基础。包括栅格数据处理、因子滞后性分析、机器学习模型建模、参数优化、模型验证。相应的方法包括栅格转化、利用时间滞后互相关法 |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN115659794A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN115659794A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN115659794A3</originalsourceid><addsrcrecordid>eNqNyjEKwkAQRuE0FqLeYTxAiqBRUkpQtLGylbAm_24WdmfC7uT-ingAq1d8b1k8b6wIwTuwUhB2pSJFSjOLtWQloTdZPTuK0FEGEkvR9KNnUIBJ_CUZEOhlMj7O5JIfaGaveV0srAkZm19XxfZyfrTXEpN0yJPpwdCuvVdVfaibY7M_7f553hNgPFw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Intelligent long-term runoff forecasting method of machine learning model based on grid units</title><source>esp@cenet</source><creator>CHU HAIBO ; WANG ZHUOQI ; WANG ZONGHAN</creator><creatorcontrib>CHU HAIBO ; WANG ZHUOQI ; WANG ZONGHAN</creatorcontrib><description>The invention discloses an intelligent long-term runoff forecasting method of a machine learning model based on grid units. The intelligent long-term runoff forecasting method takes the theory and method of machine learning as the basis of modeling. Comprising the steps of raster data processing, factor hysteresis analysis, machine learning model modeling, parameter optimization and model verification. The corresponding method comprises the steps of grid conversion, utilization of a time delay cross-correlation method, a machine learning model and a verification method. According to the medium-and-long-term runoff forecasting method provided by the invention, the determination coefficients are used for evaluation, and the determination coefficient values can reach 0.8 or above and reach the second-class precision or above of the hydrological information forecasting specification (GB/T222482-2008).
本发明公开了一种基于栅格单元的机器学习模型的智能长期径流预报方法,本发明以机器学习的理论和方法作为建模的基础。包括栅格数据处理、因子滞后性分析、机器学习模型建模、参数优化、模型验证。相应的方法包括栅格转化、利用时间滞后互相关法</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230131&DB=EPODOC&CC=CN&NR=115659794A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,778,883,25547,76298</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230131&DB=EPODOC&CC=CN&NR=115659794A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>CHU HAIBO</creatorcontrib><creatorcontrib>WANG ZHUOQI</creatorcontrib><creatorcontrib>WANG ZONGHAN</creatorcontrib><title>Intelligent long-term runoff forecasting method of machine learning model based on grid units</title><description>The invention discloses an intelligent long-term runoff forecasting method of a machine learning model based on grid units. The intelligent long-term runoff forecasting method takes the theory and method of machine learning as the basis of modeling. Comprising the steps of raster data processing, factor hysteresis analysis, machine learning model modeling, parameter optimization and model verification. The corresponding method comprises the steps of grid conversion, utilization of a time delay cross-correlation method, a machine learning model and a verification method. According to the medium-and-long-term runoff forecasting method provided by the invention, the determination coefficients are used for evaluation, and the determination coefficient values can reach 0.8 or above and reach the second-class precision or above of the hydrological information forecasting specification (GB/T222482-2008).
本发明公开了一种基于栅格单元的机器学习模型的智能长期径流预报方法,本发明以机器学习的理论和方法作为建模的基础。包括栅格数据处理、因子滞后性分析、机器学习模型建模、参数优化、模型验证。相应的方法包括栅格转化、利用时间滞后互相关法</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyjEKwkAQRuE0FqLeYTxAiqBRUkpQtLGylbAm_24WdmfC7uT-ingAq1d8b1k8b6wIwTuwUhB2pSJFSjOLtWQloTdZPTuK0FEGEkvR9KNnUIBJ_CUZEOhlMj7O5JIfaGaveV0srAkZm19XxfZyfrTXEpN0yJPpwdCuvVdVfaibY7M_7f553hNgPFw</recordid><startdate>20230131</startdate><enddate>20230131</enddate><creator>CHU HAIBO</creator><creator>WANG ZHUOQI</creator><creator>WANG ZONGHAN</creator><scope>EVB</scope></search><sort><creationdate>20230131</creationdate><title>Intelligent long-term runoff forecasting method of machine learning model based on grid units</title><author>CHU HAIBO ; WANG ZHUOQI ; WANG ZONGHAN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115659794A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>CHU HAIBO</creatorcontrib><creatorcontrib>WANG ZHUOQI</creatorcontrib><creatorcontrib>WANG ZONGHAN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>CHU HAIBO</au><au>WANG ZHUOQI</au><au>WANG ZONGHAN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Intelligent long-term runoff forecasting method of machine learning model based on grid units</title><date>2023-01-31</date><risdate>2023</risdate><abstract>The invention discloses an intelligent long-term runoff forecasting method of a machine learning model based on grid units. The intelligent long-term runoff forecasting method takes the theory and method of machine learning as the basis of modeling. Comprising the steps of raster data processing, factor hysteresis analysis, machine learning model modeling, parameter optimization and model verification. The corresponding method comprises the steps of grid conversion, utilization of a time delay cross-correlation method, a machine learning model and a verification method. According to the medium-and-long-term runoff forecasting method provided by the invention, the determination coefficients are used for evaluation, and the determination coefficient values can reach 0.8 or above and reach the second-class precision or above of the hydrological information forecasting specification (GB/T222482-2008).
本发明公开了一种基于栅格单元的机器学习模型的智能长期径流预报方法,本发明以机器学习的理论和方法作为建模的基础。包括栅格数据处理、因子滞后性分析、机器学习模型建模、参数优化、模型验证。相应的方法包括栅格转化、利用时间滞后互相关法</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN115659794A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
title | Intelligent long-term runoff forecasting method of machine learning model based on grid units |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T19%3A28%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=CHU%20HAIBO&rft.date=2023-01-31&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN115659794A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |