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Article

Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region

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Departamento de Engenharia Agrícola, Campus do Pici, Universidade Federal do Ceará, Fortaleza 60455-760, CE, Brazil
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Departamento de Conservação de Solo e Água, Universidade Federal Rural do Semi-Árido, Rua Francisco Mota, 572, Mossoró 59625-900, CE, Brazil
3
FP-ENAS, Universidade Fernando Pessoa, Praça 9 Abril, 4249-004 Porto, Portugal
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Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Rodovia Iguatu-Várzea Alegre, km 5, Iguatu 63503-790, CE, Brazil
*
Author to whom correspondence should be addressed.
Academic Editor: Georgios Koubouris
Sustainability 2022, 14(2), 675; https://doi.org/10.3390/su14020675
Received: 18 December 2021 / Revised: 3 January 2022 / Accepted: 4 January 2022 / Published: 8 January 2022
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. View Full-Text
Keywords: artificial intelligence; swelling and shrinking; Vertisol; tropical dry regions artificial intelligence; swelling and shrinking; Vertisol; tropical dry regions
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MDPI and ACS Style

Ribeiro Filho, J.C.; de Andrade, E.M.; Guerreiro, M.S.; de Queiroz Palácio, H.A.; Brasil, J.B. Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region. Sustainability 2022, 14, 675. https://doi.org/10.3390/su14020675

AMA Style

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

Chicago/Turabian Style

Ribeiro Filho, Jacques Carvalho, Eunice Maia de Andrade, Maria Simas Guerreiro, Helba Araujo de Queiroz Palácio, and José Bandeira Brasil. 2022. "Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region" Sustainability 14, no. 2: 675. https://doi.org/10.3390/su14020675

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