基于变量重要性评分-随机森林的溶解氧预测模型——以深圳湾为例

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Abstract A real-time prediction model for dissolved oxygen was established by using Pearson correlation analysis, variable importance measures and random forest method. Taking Shenzhen Bay as an example, the model was used to predict the dissolved oxygen in 1h, 3h, 6h and 12h based on the buoy data. The results showed that the optimal input conditions of the model were pH, water temperature, chlorophyll A, redox potential and blue-green algae. The correlation coefficient of 1h prediction results was more than 0.9, and the 6h prediction results could meet the engineering requirements to a certain extent. However, the prediction of low dissolved oxygen events might be within 3h.

Key words： dissolved oxygen prediction model variable importance measures random forest

Received: 17 January 2022

PACS:

X832

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	YANG Ming-yue
	MAO Xian-zhong

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YANG Ming-yue,MAO Xian-zhong. Dissolved oxygen prediction model based on variable importance measures and random forest: A case study of Shenzhen Bay[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(8): 3876-3881.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I8/3876

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