Digital Soil Mapping Using Artificial Neural Networks and Terrain-Related Attributes
References (56)
Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach
Eur J Agron
(2009)- et al.
Estimation of heavy metal sorption in German soils using artificial neural networks
Geoderma
(2009) Photo-interpretation of land facets as a soil mapping technique
Geoforum
(1974)- et al.
Using Canonical Correspondence Analysis (CCA) to identify the most important DEM attributes for digital soil mapping applications
Catena
(2011) - et al.
Simulation for response of crop yield to soil moisture and salinity with artificial neural network
Field Crop Res
(2011) - et al.
Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity
Int J Therm Sci
(2010) - et al.
The effect of survey density on the results of geopedological approach in soil mapping: A case study in the Borujen region, Central Iran
Catena
(2009) - et al.
Extrapolating regional soil landscapes from an existing soil map: Sampling intensity, validation procedures, and integration of spatial context
Geoderma
(2008) - et al.
Development of soft computing and applications in agricultural and biological engineering
Comput Electron Agr
(2010) - et al.
Artificial neural networks for corn and soybean yield prediction
Agr Syst
(2005)
Estimation of surface shear strength in Zagros region of Iran—A comparison of artificial neural networks and multiple-linear regression models
Geoderma
Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas
Comput Electron Agr
Artificial neural networks of soil erosion and runoff prediction at the plot scale
Catena
On digital soil mapping
Geoderma
Spatial prediction of soil properties using environmental correlation
Geoderma
Predict soil erosion with artificial neural network in Tanakami (Japan)
WSEAS T Comput
Using complex permittivity and artificial neural networks to identify and classify copper, zinc, and lead contamination in soil
Can Geotech J
Neural network models to predict cation exchange capacity in arid regions of Iran
Eur J Soil Sci
Applied Land Evaluation and Land Use Planning (in Persian)
Prediction of swelling pressures of expansive soils using artificial neural networks
Adv Eng Softw
Digital soil mapping using artificial neural networks
J Plant Nutr Soil Sci
Neural Networks for Pattern Recognition
A quantitative method to test for consistency and correctness in photointerpretation
Photogramm. Eng Rem Sens
Guidelines for Evaluating the Adequacy of Soil Resource Inventories
On the application of inductive machine learning tools to geographical analysis
Geogr Anal
Is inductive machine learning just another wild goose (or might it lay the golden egg)?
Int J Geogr Inf Sci
Modeling soil-landscape and ecosystem properties using terrain attributes
Soil Sci Soc Am J
Geological Map of Borujen (Sheet No. 6253)
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Carbon sequestration potential in croplands in Lesotho
2022, Ecological ModellingCitation Excerpt :The regmodel Suite function in R software was used to assess different models and select the best regression method. Ten machine learning approaches were tested:`1 Linear Model (GLM) (Nelder, 1977), Random Forest (RF) (Breiman, 2001), Support Vector Machine (SVM) (Vapnik, 2000), Boosting generalized linear model (BGLM) (), Classification and regression tree with bagging (BCART) (Breiman, 1996), Cubist (Quinlan et al., 1993), Classification and regression tree (CART),(Breiman et al., 1984), Ranger Random Forest (Ranger) (Wright and Ziegler, 2017), quantile regression forest (QRF) (Vaysse and Lagacherie, 2017), and quantitative neural network (QNR) (Bagheri Bodaghabadi et al., 2015). Cubist, RF, QRF, Ranger, CART and BCART are decision tree algorithms that differ based on their ways of dealing with variance reduction (Padarian et al., 2020).