Identification rules of wheat Cd risk based on Bayesian decision tree
TONG Gui-jie1, WU Shao-hua2, YUAN Yu-jie1, YAN Dao-hao1, ZHOU Sheng-lu1, LI Fu-fu1
1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; 2. Institute of Land and Urban-rural Development, Zhejiang University of Finance & Economics, Hangzhou 310018, China
Abstract:In order to reveal the relationship between the environmental factors and the risk of wheat excessive Cd, seven factors (concentration of Cd in soil, polluting enterprises, the town and industrial land, transportation land, soil type, SOM and soil pH) are considered, and five Bayesian decision trees were established based on ID3algorithm and Naive Bayesian algorithm. 15 identification rules of wheat Cd pollution risk were extracted, and the risk was divided into five levels. Polluting enterprises, soil pH and concentration of Cd in soil were the three dominant factors of wheat Cd enrichment. According to the validation, the average prediction accuracy was 81.14%, and the overall recognition accuracy was improved to 89.32% after using the Bayesian algorithm and risk identification rules. The model integrated the Bayesian algorithm into the decision tree model, which could assess the Cd pollution risk in samples with complete or missing data, determine the dominant factors of wheat Cd enrichment, and identify the degree and region of wheat Cd pollution based on the risk identification rules. This approach could provide a scientific tool for soil safety use and the delimitation of wheat safety production area.
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