Spatial analysis of clay content in soils using neurocomputing and pedological support: a case study of Valle Telesina (South Italy)

The spatial analysis of soil properties by means of quantitative methods is useful to make predictions at sampled and unsampled locations. Two most important characteristics are tackled, namely the option of using complex and nonlinear models in contrast with (also very simple) linear approaches, and the opportunity to build spatial inference tools using horizons as basic soil components. The objective is to perform the spatial analysis of clay content for validation purposes in order to understand whether nonlinear methods can manage soil horizons, and to quantitatively measure how much they outperform simpler methods. This is addressed in a case study in which relatively few records are available to calibrate (train) such complex models. We built three models which are based on artificial neural networks, namely single artificial neural networks, median neural networks and bootstrap aggregating neural networks with genetic algorithms and principal component regression (BAGAP). We perform a validation procedure at three different levels of soil horizon aggregations (i.e. topsoil, profile and horizon pedological supports). The results show that neurocomputing performs best at any level of pedological support even when we use an ensemble of neural nets (i.e. BAGAP), which is very data intensive. BAGAP has the lowest RMSE at any level of pedological support with $$\hbox {RMSE}_\mathrm{BAGAP}^{Topsoil} = 7.2\,\%$$ , $$\hbox {RMSE}_\mathrm{BAGAP}^{Profile} = 7.8\,\%$$ and $$\hbox {RMSE}_\mathrm{BAGAP}^{Horizon} = 8.8\,\%$$ . We analysed in-depth artificial neural parameters, and included them in the “Appendix”, to provide the best tuned neural-based model to enable us to make suitable spatial predictions.