| dc.description.abstract | Taoyuan City has the second largest area of land pollution in the country. Despite the government′s active efforts over the years to remediate most of the contaminated land, the characteristics of the land continue to change, and farmland remains susceptible to potential recontamination. Currently, there is no dedicated method for predicting heavy metal recontamination specifically for Taoyuan City′s regulated farmland. Therefore, this study focuses on cadmium, copper, and zinc, three heavy metals, to establish a random forest model using Python, based on changes in soil monitoring concentrations and soil heavy metal concentration increments. A comprehensive literature review indicates the factors, followed by data collection which involves 1,555 datasets in recent 18 years (2004-2021). There are four common prediction methods applied and random forest performed the best at an accuracy of 75.76% with 0.05% of error, demonstrating a certain level of reliability. Furthermore, potential applications of the model are proposed, with the hope that relevant agencies in the future can use the constructed model to predict potential recontamination sites and enable targeted deployment of monitoring and control measures by environmental protection units, thereby avoiding unnecessary extensive testing and conserving resources such as time, manpower, and finances. | en_US |