Uso de inteligência artificial para estimativa da capacidade de suporte de carga do solo

• Ano de defesa: 2017
• Autor(a) principal: Pereira, Tonismar dos Santos
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/26339/001300000s6hm
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Engenharia Agrícola; UFSM; Programa de Pós-Graduação em Engenharia Agrícola; Centro de Ciências Rurais
• Palavras-chave em Português: Pedofunções; Compactação do solo; Redes neurais artificiais; Máquinas de vetores de suporte; Inteligência artificial; Pedofunctions; Soil compaction; Artificial neural networks; Support vector machine; Artificial intelligence; CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
• Link de acesso: http://repositorio.ufsm.br/handle/1/11390

The knowledge of the relationships between physical and mechanical properties of the soil may contribute to the development of pedotransfer functions (PTFs), to estimate other soil properties are difficult to measure. The objectives of this work were to estimate the preconsolidation pressure and soil resistance to penetration, using predictive methodologies, using data available in the literature, with physical-hydrological and mineralogical characteristics of soils. The development of PTFs was based on three modeling methods: (i) multiple linear regression (MLR), (ii) artificial neural networks (ANNs) and (iii) support vector machines (SVM). The first proposed methodology for the development of PTFs was the stepwise option of the IBM-SPSS 20.0® software. The models generated from the second methodology, ie RNA were implemented through the multilayer perceptron with backpropagation algorithm and Levenberg-Marquardt optimization of Matlab®2008b software, with variations of the number of neurons in the input layer and number of neurons In the middle layer. The third methodology was to generate PTFs from SVM that fit within the data mining process by exercising the Waikato Environment for Knowledge Analysis software (RapidMiner 5). The SVM training was performed by varying the number of input data, the kernel function and coefficients of these functions. Once the estimates were made, the performance indices (id) and classified according to Camargo and Sentelhas (1997) were calculated, thus comparing the methods between themselves and others already established. The obtained results showed that artificial intelligence models (RNA and MVS) are efficient and have predictive capacity superior to the established models, in data conditions of soils with textural classes and diverse managements, and similar, although with higher performance index values for Conditions of soils of the same textural class exposed to the same management.