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

Jacques Carvalho Ribeiro Filho, Eunice Maia de Andrade, Maria Simas Guerreiro, Helba Araujo de Queiroz Palácio and José Bandeira Brasil
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Jacques Carvalho Ribeiro Filho: Departamento de Engenharia Agrícola, Campus do Pici, Universidade Federal do Ceará, Fortaleza 60455-760, CE, Brazil
Eunice Maia de Andrade: Departamento de Conservação de Solo e Água, Universidade Federal Rural do Semi-Árido, Rua Francisco Mota, 572, Mossoró 59625-900, CE, Brazil
Maria Simas Guerreiro: FP-ENAS, Universidade Fernando Pessoa, Praça 9 Abril, 4249-004 Porto, Portugal
Helba Araujo de Queiroz Palácio: Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Rodovia Iguatu-Várzea Alegre, km 5, Iguatu 63503-790, CE, Brazil
José Bandeira Brasil: Departamento de Engenharia Agrícola, Campus do Pici, Universidade Federal do Ceará, Fortaleza 60455-760, CE, Brazil

Sustainability, 2022, vol. 14, issue 2, 1-16

Abstract: 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.

Keywords: artificial intelligence; swelling and shrinking; Vertisol; tropical dry regions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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