Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10284/11036 |
Resumo: | The 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. |
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Climate data to predict geometry of cracks in expansive soils in a tropical semiarid regionArtificial intelligenceSwelling and shrinkingVertisolTropical dry regionsThe 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.MDPIRepositório Institucional da Universidade Fernando PessoaRibeiro Filho, Jacques CarvalhoAndrade, Eunice Maia deGuerreiro, Maria Joãode Queiroz Palácio, Helba AraujoBrasil, José Bandeira2022-07-26T16:25:22Z2022-01-08T00:00:00Z2022-01-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10284/11036engRibeiro 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/su1402067510.3390/su140206752071-1050info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-09-06T02:10:20Zoai:bdigital.ufp.pt:10284/11036Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T15:47:47.275984Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region |
title |
Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region |
spellingShingle |
Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region Ribeiro Filho, Jacques Carvalho Artificial intelligence Swelling and shrinking Vertisol Tropical dry regions |
title_short |
Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region |
title_full |
Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region |
title_fullStr |
Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region |
title_full_unstemmed |
Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region |
title_sort |
Climate data to predict geometry of cracks in expansive soils in a tropical semiarid region |
author |
Ribeiro Filho, Jacques Carvalho |
author_facet |
Ribeiro Filho, Jacques Carvalho Andrade, Eunice Maia de Guerreiro, Maria João de Queiroz Palácio, Helba Araujo Brasil, José Bandeira |
author_role |
author |
author2 |
Andrade, Eunice Maia de Guerreiro, Maria João de Queiroz Palácio, Helba Araujo Brasil, José Bandeira |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório Institucional da Universidade Fernando Pessoa |
dc.contributor.author.fl_str_mv |
Ribeiro Filho, Jacques Carvalho Andrade, Eunice Maia de Guerreiro, Maria João de Queiroz Palácio, Helba Araujo Brasil, José Bandeira |
dc.subject.por.fl_str_mv |
Artificial intelligence Swelling and shrinking Vertisol Tropical dry regions |
topic |
Artificial intelligence Swelling and shrinking Vertisol Tropical dry regions |
description |
The 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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07-26T16:25:22Z 2022-01-08T00:00:00Z 2022-01-08T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10284/11036 |
url |
http://hdl.handle.net/10284/11036 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ribeiro 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/su14020675 10.3390/su14020675 2071-1050 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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