Data-Driven Modeling of Groundwater Level with Least-Square Support Vector Machine and Spatial–Temporal Analysis
Investigation of groundwater level is considered a prominent research topic for the study of underground hydrologic system. Due to the complexities of underground geological structure, the accuracy of real-time ground water level prediction is limited. In this study, a novel two-phase data-driven fr...
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Veröffentlicht in: | Geotechnical and geological engineering 2019-06, Vol.37 (3), p.1661-1670 |
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creator | Tang, Yandong Zang, Cuiping Wei, Yong Jiang, Minghui |
description | Investigation of groundwater level is considered a prominent research topic for the study of underground hydrologic system. Due to the complexities of underground geological structure, the accuracy of real-time ground water level prediction is limited. In this study, a novel two-phase data-driven framework to model the time-series groundwater level with spatial–temporal analysis and least square support vector machine is proposed. Groundwater data collected from four monitoring sites in the northern region of United Kingdom is utilize in this study. In phase I, the time-series analysis is conducted to study the temporal characteristics of the groundwater. Based on the time-series analysis, least square support vector machine is performed to construct the prediction model to forecast the future groundwater level. In phase-II, the spatial correlation between the water levels in four sites are computed to construct a comprehensive model regarding the interrelation between the monitoring sites. Computational results illustrated the outperformance of least square support vector machine in predicting time-series groundwater levels compared with other state-of-arts machine learning algorithms. It has been demonstrated that the spatial–temporal model may serve as an applicable approach for the future research of groundwater resources. |
doi_str_mv | 10.1007/s10706-018-0713-6 |
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Due to the complexities of underground geological structure, the accuracy of real-time ground water level prediction is limited. In this study, a novel two-phase data-driven framework to model the time-series groundwater level with spatial–temporal analysis and least square support vector machine is proposed. Groundwater data collected from four monitoring sites in the northern region of United Kingdom is utilize in this study. In phase I, the time-series analysis is conducted to study the temporal characteristics of the groundwater. Based on the time-series analysis, least square support vector machine is performed to construct the prediction model to forecast the future groundwater level. In phase-II, the spatial correlation between the water levels in four sites are computed to construct a comprehensive model regarding the interrelation between the monitoring sites. Computational results illustrated the outperformance of least square support vector machine in predicting time-series groundwater levels compared with other state-of-arts machine learning algorithms. It has been demonstrated that the spatial–temporal model may serve as an applicable approach for the future research of groundwater resources.</description><identifier>ISSN: 0960-3182</identifier><identifier>EISSN: 1573-1529</identifier><identifier>DOI: 10.1007/s10706-018-0713-6</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Algorithms ; Civil Engineering ; Computer applications ; Data ; Earth and Environmental Science ; Earth Sciences ; Frameworks ; Geological structures ; Geotechnical Engineering & Applied Earth Sciences ; Groundwater ; Groundwater data ; Groundwater levels ; Hydrogeology ; Hydrologic data ; Hydrology ; Learning algorithms ; Least squares ; Machine learning ; Mathematical models ; Modelling ; Monitoring ; Original Paper ; Prediction models ; Spatial analysis ; Support vector machines ; Terrestrial Pollution ; Time series ; Underground structures ; Waste Management/Waste Technology ; Water levels ; Water resources</subject><ispartof>Geotechnical and geological engineering, 2019-06, Vol.37 (3), p.1661-1670</ispartof><rights>Springer Nature Switzerland AG 2018</rights><rights>Copyright Springer Nature B.V. 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a339t-4b5aa38b1204e44e28cfefa2d9dda46b6cb42ffbeae317ab3aac3f18fc41b60e3</citedby><cites>FETCH-LOGICAL-a339t-4b5aa38b1204e44e28cfefa2d9dda46b6cb42ffbeae317ab3aac3f18fc41b60e3</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/s10706-018-0713-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10706-018-0713-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Tang, Yandong</creatorcontrib><creatorcontrib>Zang, Cuiping</creatorcontrib><creatorcontrib>Wei, Yong</creatorcontrib><creatorcontrib>Jiang, Minghui</creatorcontrib><title>Data-Driven Modeling of Groundwater Level with Least-Square Support Vector Machine and Spatial–Temporal Analysis</title><title>Geotechnical and geological engineering</title><addtitle>Geotech Geol Eng</addtitle><description>Investigation of groundwater level is considered a prominent research topic for the study of underground hydrologic system. Due to the complexities of underground geological structure, the accuracy of real-time ground water level prediction is limited. In this study, a novel two-phase data-driven framework to model the time-series groundwater level with spatial–temporal analysis and least square support vector machine is proposed. Groundwater data collected from four monitoring sites in the northern region of United Kingdom is utilize in this study. In phase I, the time-series analysis is conducted to study the temporal characteristics of the groundwater. Based on the time-series analysis, least square support vector machine is performed to construct the prediction model to forecast the future groundwater level. In phase-II, the spatial correlation between the water levels in four sites are computed to construct a comprehensive model regarding the interrelation between the monitoring sites. Computational results illustrated the outperformance of least square support vector machine in predicting time-series groundwater levels compared with other state-of-arts machine learning algorithms. It has been demonstrated that the spatial–temporal model may serve as an applicable approach for the future research of groundwater resources.</description><subject>Algorithms</subject><subject>Civil Engineering</subject><subject>Computer applications</subject><subject>Data</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Frameworks</subject><subject>Geological structures</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Groundwater</subject><subject>Groundwater data</subject><subject>Groundwater levels</subject><subject>Hydrogeology</subject><subject>Hydrologic data</subject><subject>Hydrology</subject><subject>Learning algorithms</subject><subject>Least squares</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Modelling</subject><subject>Monitoring</subject><subject>Original Paper</subject><subject>Prediction models</subject><subject>Spatial analysis</subject><subject>Support vector machines</subject><subject>Terrestrial Pollution</subject><subject>Time series</subject><subject>Underground structures</subject><subject>Waste Management/Waste Technology</subject><subject>Water levels</subject><subject>Water resources</subject><issn>0960-3182</issn><issn>1573-1529</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kM9OwkAQhzdGExF9AG-beF7df7TlSEDRBOIB9LqZtrNQUtqyu5Vw8x18Q5_EEkw8mTnMHL7fL5OPkFvB7wXn8YMXPOYR4yJhPBaKRWekJwaxYmIgh-ekx4cRZ0ok8pJceb_hnMuIix5xEwjAJq74wIrO6xzLolrR2tKpq9sq30NAR2f4gSXdF2HdneADW-xacEgXbdPULtB3zELt6ByydVEhhSqniwZCAeX359cStx0EJR1VUB584a_JhYXS483v7pO3p8fl-JnNXqcv49GMgVLDwHQ6AFBJKiTXqDXKJLNoQebDPAcdpVGWamltioBKxJAqgExZkdhMizTiqPrk7tTbuHrXog9mU7eue8IbKYXuJpG6o8SJylztvUNrGldswR2M4Oao1pzUmk6tOao1UZeRp4zv2GqF7q_5_9APEn9_KA</recordid><startdate>20190601</startdate><enddate>20190601</enddate><creator>Tang, Yandong</creator><creator>Zang, Cuiping</creator><creator>Wei, Yong</creator><creator>Jiang, Minghui</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TN</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>20190601</creationdate><title>Data-Driven Modeling of Groundwater Level with Least-Square Support Vector Machine and Spatial–Temporal Analysis</title><author>Tang, Yandong ; Zang, Cuiping ; Wei, Yong ; Jiang, Minghui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a339t-4b5aa38b1204e44e28cfefa2d9dda46b6cb42ffbeae317ab3aac3f18fc41b60e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Civil Engineering</topic><topic>Computer applications</topic><topic>Data</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Frameworks</topic><topic>Geological structures</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Groundwater</topic><topic>Groundwater data</topic><topic>Groundwater levels</topic><topic>Hydrogeology</topic><topic>Hydrologic data</topic><topic>Hydrology</topic><topic>Learning algorithms</topic><topic>Least squares</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Modelling</topic><topic>Monitoring</topic><topic>Original Paper</topic><topic>Prediction models</topic><topic>Spatial analysis</topic><topic>Support vector machines</topic><topic>Terrestrial Pollution</topic><topic>Time series</topic><topic>Underground structures</topic><topic>Waste Management/Waste Technology</topic><topic>Water levels</topic><topic>Water resources</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tang, Yandong</creatorcontrib><creatorcontrib>Zang, Cuiping</creatorcontrib><creatorcontrib>Wei, Yong</creatorcontrib><creatorcontrib>Jiang, Minghui</creatorcontrib><collection>CrossRef</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>Geotechnical and geological engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tang, Yandong</au><au>Zang, Cuiping</au><au>Wei, Yong</au><au>Jiang, Minghui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data-Driven Modeling of Groundwater Level with Least-Square Support Vector Machine and Spatial–Temporal Analysis</atitle><jtitle>Geotechnical and geological engineering</jtitle><stitle>Geotech Geol Eng</stitle><date>2019-06-01</date><risdate>2019</risdate><volume>37</volume><issue>3</issue><spage>1661</spage><epage>1670</epage><pages>1661-1670</pages><issn>0960-3182</issn><eissn>1573-1529</eissn><abstract>Investigation of groundwater level is considered a prominent research topic for the study of underground hydrologic system. Due to the complexities of underground geological structure, the accuracy of real-time ground water level prediction is limited. In this study, a novel two-phase data-driven framework to model the time-series groundwater level with spatial–temporal analysis and least square support vector machine is proposed. Groundwater data collected from four monitoring sites in the northern region of United Kingdom is utilize in this study. In phase I, the time-series analysis is conducted to study the temporal characteristics of the groundwater. Based on the time-series analysis, least square support vector machine is performed to construct the prediction model to forecast the future groundwater level. In phase-II, the spatial correlation between the water levels in four sites are computed to construct a comprehensive model regarding the interrelation between the monitoring sites. 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subjects | Algorithms Civil Engineering Computer applications Data Earth and Environmental Science Earth Sciences Frameworks Geological structures Geotechnical Engineering & Applied Earth Sciences Groundwater Groundwater data Groundwater levels Hydrogeology Hydrologic data Hydrology Learning algorithms Least squares Machine learning Mathematical models Modelling Monitoring Original Paper Prediction models Spatial analysis Support vector machines Terrestrial Pollution Time series Underground structures Waste Management/Waste Technology Water levels Water resources |
title | Data-Driven Modeling of Groundwater Level with Least-Square Support Vector Machine and Spatial–Temporal Analysis |
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