Delineation of Heavy Metal Pollution Potential Areas in Agricultural Land Using Support Vector Machine Analysis
Date Issued
2015
Date
2015
Author(s)
Liu, Xiu-Ming
Abstract
Support Vector Machine (SVM) is a kind of supervised learning algorithm of machine learning in computer science, it’s function such as classification and regression could also be applied to related field e.g. geoscience and environmental science. In this research, the data of heavy metal pollution areas in agricultural land converted by Nemerow index (PN) combined with SVM and geographic information system (GIS) classifies the highly potential heavy metal pollution areas in agricultural land. For modeling, the samples were optimized into an ideal proportion for training data set by using 10-fold cross validation. In Changhua County, at 7,353 points with the sample labeled ratio of positive (PN≧1.0) and negative (PN<1.0) set to 0.5, results show the potential heavy metal pollution area with an accuracy of 85.37% and F1-measure of 0.692; In Taoyuan city, at 3,288 points with sample labeled ratio set to 1, results show the potential heavy metal pollution area with accuracy of 71.58% and F1-measure of 0.506. By interpreting the mapping of results with the information of surrounding geological features such as the distribution of river basins, factories and industrial zones, it allowed us to divide the causes and relationships of potential heavy metal polluted area with the use of SVM. Thus, the algorithm had proved that it could be validly applied to classify the potential heavy metal pollution areas in agricultural land even with low training data set.
Subjects
Support Vector Machine
heavy metals
soil pollution
pollution potential map
SDGs
Type
thesis
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