A Com-Gis Based Decision Tree Model Inagricultural Application

The problem of agricultural soil pollution by heavy metals has been receiving an increasing attention in the last few decades. Geostatistics module in ArcGIS, could not however efficiently simulate the spatial distribution of heavy metals with satisfied accuracy when the spatial autocorrelation of the study area severely destroyed by human activities. In this study, the classificationand regression tree (CART) has been integrated into ArcGIS using ArcObjects and Visual Basic for Application (VBA) to predict the spatial distribution of soil heavy metals contents in the area severely polluted. This is a great improvement comparing with ordinary Kriging method in ArcGIS. The integrated approach allows for relatively easy, fast, and cost-effective estimation of spatially distributed soil heavy metals pollution.

[1]  Shiv O. Prasher,et al.  Measuring performance in precision agriculture: CART-A decision tree approach , 2006 .

[2]  Joseph L. Breault,et al.  Data mining a diabetic data warehouse , 2002, Artif. Intell. Medicine.

[3]  Raghavan Srinivasan,et al.  A GIS‐Coupled Hydrological Model System for the Watershed Assessment of Agricultural Nonpoint and Point Sources of Pollution , 2004, Trans. GIS.

[4]  João Aires-de-Sousa,et al.  Prediction of enantioselectivity using chirality codes and Classification and Regression Trees , 2005 .

[5]  Tian-Shyug Lee,et al.  Mining the customer credit using classification and regression tree and multivariate adaptive regression splines , 2006, Comput. Stat. Data Anal..

[6]  Mevlut Ture,et al.  Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease , 2008, Expert Syst. Appl..

[7]  Brett A. Bryan,et al.  CREDOS: A Conservation Reserve Evaluation And Design Optimisation System , 2007, Environmental Modelling & Software.

[8]  U. S. Tim,et al.  AN INTERACTWE MODELING ENVIRONMENT FOR NON‐POINT SOURCE POLLUTION CONTROL 1 , 1997 .

[9]  O A Al Khashman.  HEAVY METAL DISTRIBUTION IN DUST, STREET DUST AND SOIL FROM THE WORK PLACE IN KARAK INDUSTRIAL ESTATE, JORDAN , 2004 .

[10]  O. Al-Khashman,et al.  Heavy metal distribution in dust, street dust and soils from the work place in Karak Industrial Estate, Jordan , 2004 .

[11]  Winfried Schröder,et al.  GIS, geostatistics, metadata banking, and tree-based models for data analysis and mapping in environmental monitoring and epidemiology. , 2006, International journal of medical microbiology : IJMM.

[12]  Rose L. Winterton,et al.  A customised GIS to aid Gondwana research , 2004 .

[13]  Tingting Yao.  Nonparametric cross-covariance modeling as exemplified by soil heavy metal concentrations from the Swiss Jura , 1999 .

[14]  R. Weil,et al.  The nature and properties of soils. 11th ed. , 1996 .

[15]  Suzana Dragicevic,et al.  iCity: A GIS-CA modelling tool for urban planning and decision making , 2007, Environ. Model. Softw..

[16]  Yong Li,et al.  Predicting materials properties and behavior using classification and regression trees , 2006 .

[17]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[18]  Jennifer M. Jacobs,et al.  A GIS-based model to estimate the regionally distributed drought water demand , 2004 .

[19]  Roland Pesch,et al.  Metal accumulation in mosses across national boundaries: uncovering and ranking causes of spatial variation. , 2008, Environmental pollution.