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

Content Provider	Springer Nature Link
Author	Langella, Giuliano Basile, Angelo Bonfante, Antonello Mileti, Florindo Antonio Terribile, Fabio
Copyright Year	2016
Abstract	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.
Starting Page	1
Ending Page	19
Page Count	19
File Format	PDF
ISSN	18666280
Journal	Environmental Earth Sciences
Volume Number	75
Issue Number	20
e-ISSN	18666299
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-10-15
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural network Pedological support Soil variability Spatial analysis Bagging Geology Hydrology/Water Resources Geochemistry Environmental Science and Engineering Terrestrial Pollution Biogeosciences
Content Type	Text
Resource Type	Article
Subject	Global and Planetary Change Earth-Surface Processes Soil Science Environmental Chemistry Pollution Geology Water Science and Technology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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