Comparison of Linear Regression Methods, Geostatistical and Artificial Neural Network Modeling of Organic Carbon in Dry Land of Sistan Plain

Knowledge of organic carbon spatial variations in different land uses will help to interpret and simulate the behavior of terrestrial ecosystems facing environmental and climate changes. The purpose of this study is comparing regression, geostatistics and artificial neural network (ANN) methods for predicting organic carbon content in 192 samples of surface soil (0 to 30 cm) of Sistan plain (Miankangi region). In this study, Only 25% of organic carbon variations were explained by variables used in linear regression model in the study area (R2= 0.25). Moreover, simple co-kriging (with clay as co-variable) which was the best geostatistical method in the current study, predicted organic carbon content weakly (R2= 0.23 and RMSE= 0.127). However, using latitude and longitude parameters, ANN performed much better than linear regression and geostatistical methods for predicting organic carbon content (R2= 0.79 and RMSE= 0.044).