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Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region

2022-01-08Journal article Open access
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dc.contributor.authorRibeiro Filho, Jacques Carvalho
dc.contributor.authorAndrade, Eunice Maia de
dc.contributor.authorGuerreiro, Maria João
dc.contributor.authorde Queiroz Palácio, Helba Araujo
dc.contributor.authorBrasil, José Bandeira
dc.date.accessioned2022-07-26T16:25:22Z
dc.date.available2022-07-26T16:25:22Z
dc.date.issued2022-01-08
dc.description.abstractThe nonlinear dynamics of the determining factors of the morphometric characteristics of cracks in expansive soils make their typification a challenge, especially under field conditions. To overcome this difficulty, we used artificial neural networks to estimate crack characteristics in a Vertisol under field conditions. From July 2019 to June 2020, the morphometric characteristics of soil cracks (area, depth and volume), and environmental factors (soil moisture, rainfall, potential evapotranspiration and water balance) were monitored and evaluated in six experimental plots in a tropical semiarid region. Sixty-six events were measured in each plot to calibrate and validate two sets of inputs in the multilayer neural network model. One set was comprised of environmental factors with significant correlations with the morphometric characteristics of cracks in the soil. The other included only those with a significant high and very high correlation, reducing the number of variables by 35%. The set with the significant high and very high correlations showed greater accuracy in predicting crack characteristics, implying that it is preferable to have fewer variables with a higher correlation than to have more variables of lower correlation in the model. Both sets of data showed a good performance in predicting area and depth of cracks in the soils with a clay content above 30%. The highest dispersion of modeled over predicted values for all morphometric characteristics was in soils with a sand content above 40%. The model was successful in evaluating crack characteristics from environmental factors within its limitations and may support decisions on watershed management in view of climate-change scenarios.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationRibeiro Filho JC, de Andrade EM, Guerreiro MS, de Queiroz Palácio HA, Brasil JB. Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region. Sustainability. 2022; 14(2):675. https://doi.org/10.3390/su14020675pt_PT
dc.identifier.doi10.3390/su14020675pt_PT
dc.identifier.eissn2071-1050
dc.identifier.urihttp://hdl.handle.net/10284/11036
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectSwelling and shrinkingpt_PT
dc.subjectVertisolpt_PT
dc.subjectTropical dry regionspt_PT
dc.titleClimate data to predict geometry of cracks in expansive soils in a tropical semiarid regionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue2pt_PT
oaire.citation.startPage675pt_PT
oaire.citation.titleSustainabilitypt_PT
oaire.citation.volume14pt_PT
person.familyNameGuerreiro
person.givenNameMaria
person.identifier.ciencia-id1216-4260-3FD7
person.identifier.orcid0000-0001-6774-9348
person.identifier.scopus-author-id23024471700
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd9ab10bc-ed17-4df2-9efc-90a3e2d0852a
relation.isAuthorOfPublication.latestForDiscoveryd9ab10bc-ed17-4df2-9efc-90a3e2d0852a

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