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Comparison of machine learning parametric and non-parametric techniques for determining soil moisture: Case study at las palmas andean basin

dc.contributor.authorLópez-Bermeo C
dc.contributor.authorGonzález-Palacio M
dc.contributor.authorSepúlveda-Cano L
dc.contributor.authorMontoya-Ramírez R
dc.contributor.authorHidalgo-Montoya C.
dc.date.accessioned2022-09-14T14:33:31Z
dc.date.available2022-09-14T14:33:31Z
dc.date.created2021
dc.identifier.issn24156698
dc.identifier.urihttp://hdl.handle.net/11407/7410
dc.descriptionSoil moisture is one of the most important variables to monitor in agriculture. Its analysis gives insights about strategies to utilize better a particular area regarding its use, i.e., pasture for cows (or similar), production forests, or even to answer what crops should be planted. The vertical structure of the soil moisture plays an important role in several physical processes such as vegetation growth, infiltration process, soil – atmosphere interactions, among others. Despite a set of tools are currently being evaluated and used to monitor soil moisture, including satellite images and in-situ sensor, several drawbacks are still persisting. In situ data is expensive for high spatial monitoring and vertical measurements and satellite data have low spatial resolution and only retrieval information of soil moisture for the top few centimeters of the soil. The present work shows an experiment design for collecting soil moisture data in a specific Andean basin with in-situ sensors in different kinds of soils as a promising tool for reproducing soil moisture profiles in areas with scarce information, employing only surface soil moisture and simple soil characteristics. Collected data is used to train machine learning supervised parametric (Multiple Linear Regression - MLR) and non-parametric models (Artificial Neural Networks - ANNs and Support Vector Regression - SVR) for soil moisture estimation in different depths. Conclusions show that parametric methods do not meet goodness of fit assumptions; so, non-parametric methods must be considered, and SVR outperforms parametric methods regarding regression accuracy allowing to reproduce the soil moisture content profiles. The proposed SVR model represents a high potential tool to replicate the soil moisture profiles using only surface information from remote sensing or in-situ data. © 2021 ASTES Publishers. All rights reserved.eng
dc.language.isoeng
dc.publisherASTES Publishers
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85101006058&doi=10.25046%2faj060170&partnerID=40&md5=93c9d140c1cfded09184311c7ef30091
dc.sourceAdvances in Science, Technology and Engineering Systems
dc.titleComparison of machine learning parametric and non-parametric techniques for determining soil moisture: Case study at las palmas andean basin
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicaciones
dc.publisher.programIngeniería Civil
dc.type.spaArtículo
dc.identifier.doi10.25046/aj060170
dc.subject.keywordMachine Learningeng
dc.subject.keywordRegressioneng
dc.subject.keywordSoil moistureeng
dc.relation.citationvolume6
dc.relation.citationissue1
dc.relation.citationstartpage636
dc.relation.citationendpage650
dc.publisher.facultyFacultad de Ingenierías
dc.affiliationLópez-Bermeo, C., Universidad de Medellín, Facultad de Ingenierías, Medellín, 050026, Colombia
dc.affiliationGonzález-Palacio, M., Universidad de Medellín, Facultad de Ingenierías, Medellín, 050026, Colombia
dc.affiliationSepúlveda-Cano, L., Universidad de Medellín, Facultad de Ingenierías, Medellín, 050026, Colombia
dc.affiliationMontoya-Ramírez, R., Universidad de Medellín, Facultad de Ingenierías, Medellín, 050026, Colombia
dc.affiliationHidalgo-Montoya, C., Universidad de Medellín, Facultad de Ingenierías, Medellín, 050026, Colombia
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dc.type.coarhttp://purl.org/coar/resource_type/c_6501
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/article
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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