Authors: Yanyan Liu*, University of West Florida, Zhiyong Hu, University of West Florida, Johan Liebens, University of West Florida, Hanhu Liu, Chengdu University of Technology
Topics: Geographic Information Science and Systems, Environmental Science, Hazards, Risks, and Disasters
Keywords: Heavy metal, Random Forest, GIS
Session Type: Poster
Presentation File: No File Uploaded
The potential hazard of heavy metals in soil has been attracted more and more attention. Prediction of a continuous heavy metal concentration surface helps identify hotspot problem areas for environmental management and remediation. In recent years, the Forest-based algorithm has become popular in solving many kinds of problems in different fields, including geoscience. Random Forest Regression is one of them works well in prediction. The paper represents an application of the Random Forest Regression algorithm in predict concentration surface using soil sample data collected the research project by Liebens et al. (2012) and other covariates data. Such as “sewer waste points”, “dry cleaner”, “Traffic data (Named AADT) and EPA TRI (toxic release inventory) in this study relate with the main way about how soil heavy metal spread. It also includes Florida DEP “major/minor emitters” and “DEP emitters” data. In this paper, we use random forest regression to predict continuous heavy metal concentration focus on Pb and Zn. The points data were divided into different kinds to calculate the density surfaces. Joined these density data with soil properties data and sampling data to create two datasets: prediction points and training points. Both are in the study area and have the same set of explanatory variables. Then run a Random Forest Regression model to get the prediction results. Finally, assess prediction accuracy with RMSE. It was found that Random Forest Regression achieved significantly higher overall accuracy and lower RMSE value.
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