Journal of Ecology and Rural Environment ›› 2019, Vol. 35 ›› Issue (11): 1475-1483.doi: 10.19741/j.issn.1673-4831.2019.0205

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Spatial Recognition and Prediction of Rice Cd Over-standard Based on Decision Tree Model

WANG Yuan-min1, CHEN Dong-xiang2, TONG Gui-jie1, YAN Dao-hao1, LI Fu-fu1, WU Shao-hua3,4   

  1. 1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China;
    2. Dongfang College, Zhejiang University of Finance and Economics, Haining 314408, China;
    3. Land and Urban-Rural Development College, Zhejiang University of Finance and Economics, Hangzhou 310018, China;
    4. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 510034, China
  • Received:2019-04-01 Published:2019-11-19

Abstract: Cadmium contamination of rice grains has become an important factor affecting the quality and safety of rice in China. To explore relationships between Cd contamination in rice and various environmental factors, spatial mapping was carried out. Eleven environmental factors were selected for study. Using a decision tree model and a reasoning mapping method, the main factors controlling Cd contamination in rice were identified in a case study area, and a spatial prediction technology for identifying rice with Cd levels in excess of the Chinese standard was established. The results show that transportation distance, soil organic matter content and amorphous iron content were the main factors correlated with Cd contamination in rice. Rice having Cd contents exceeding the standard in the study area were extracted and classified into five grades according to their associated risk degree. The empirical accuracy of this classification method was 85.71%. In addition, a subordinate degree spatial analysis method was used to make spatial predictions of rice areas having Cd levels exceeding the standard threshold, based on information from limited sampling points. The classification accuracy using this method reached to 91.67%, representing a marked improvement, compared with the simple decision tree and traditional interpolation mapping results. However, the decision tree model developed in this study predicts the spatial distribution of Cd contamination risk in rice, making it of practical significance to mitigating Cd contamination risks in rice.

Key words: decision tree, Cd, rice grain, recognition rule, subordination mapping

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