An artificial neural network
based model to predict spatial soil type distribution using piezocone penetration test data (CPTu)
New Jornal paper: Ghaderi, A., Shahri, A. and Larsson, S. (2018) An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu). Bulletin of Engineering Geology and the Environment . doi.org/10.1007/s10064-018-1400-9
In this paper, a new optimized multi-output generalized feed forward neural network GFNN structure using piezocone penetration tests for producing a digital soil types map in the southwest of Sweden is developed. The results show that the predictability of the GFNN system offers a valuable tool for the purpose of soil type pattern classifications and providing soil profiles. The paper is published open access .