Chinese Journal of Agrometeorology ›› 2021, Vol. 42 ›› Issue (05): 390-401.doi: 10.3969/j.issn.1000-6362.2021.05.004

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Influence of Environmental Factors on Negative Air Ion Using Random Forest Algorithm

SHI Guang-yao, ZHOU Yu, SANG Yu-qiang, ZHANG Jin-song, MENG Ping, CAI Lu-lu, PEI Song-yi, WANG Yao   

  1. 1. Research Institute of Forestry, Chinese Academy of Forestry/Key Laboratory of Tree Breeding and Cultivation, State Forestry Administration, Beijing 100091, China; 2. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037; 3. Henan Agricultural University, Zhengzhou 450002; 4. Henan Geophysical Space Information Research Institute, Zhengzhou 450016; 5. State Owned Jianping County Heishui Mechanized Forest Farm, Chaoyang 122000
  • Received:2020-10-25 Online:2021-05-20 Published:2021-05-16

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Abstract: Negative air ion(NAI) is an essential indicator for measuring the air cleanliness of a given area, which plays an important role in promoting the psychological and physiological functions of the human body. With the development of forest eco-tourism, NAI has attracted substantial attention, while research on NAI has become increasingly active, especially for the topics of the occurrence process and impact mechanism of NAI in related fields. Based on meteorological data and observation data of NAI during the main growing season of Quercus variabilis BI. plantation in the hilly area of North China, the random forest model in machine learning was used to analyze the environmental factors that affected NAI concentration changes from a non-linear view, and independent samples were used to simulate the random forest model to determine the prediction accuracy of the model. The estimation model of NAI was established for revealing the response mechanism and predicting the response pattern of NAI to environmental factors for further research. Results showed that the random forest model had higher accuracy and better fitting effect in analyzing the impact of environmental factors on NAI, and by verifying the fitted and measured values of the model, the root mean square error(RMSE) was 59.349, and the coefficient of determination R2 reached 0.887. While using independent test data to 10-fold cross-validation of the random forest model, the average R2 was above 0.904 and the root mean square error(RMSE) was small at 24.851. In addition, the model screened out that the main factors affecting NAI were particulate matter PM2.5(48.037), vapor pressure deficit(46.169), soil moisture(43.984), wind speed(43.779), ultraviolet radiation(41.130), soil temperature(40.107), direct radiation(36.838) and atmospheric pressure(34.532) sorted by importance scores. Among them, the three variables contributed relatively high importance to the model were particulate matter PM2.5, vapor pressure deficit and wind speed, which prove that they play a decisive role in the variations of NAI. Therefore, the random forest model is better to simulate the NAI with high accuracy and strong stability.

Key words: Negative air ion, Environment factor, Machine learning Random forest model, Quercus variabilis BI.