基于置信规则库的空气质量预测模型

Air Quality Prediction Model Based on Confidence Rule Base

摘要: 空气质量一直是公众和社会最关注的环境问题之一，其质量已经因为经济活动而受到损害。然而，由于空气的复杂性以及人类活动对空气的影响，预测空气质量是一项不小的挑战。传统上使用单一的机器学习模型预测空气质量时，在各种AQI波动趋势下难以获得良好的预测结果。为了有效地解决这一问题，本文提出了一种分层置信规则库(Hierarchy Belief Rule Base-RFI)预测模型，为空气质量预测提供了一种有用的方法。首先，为了提高模型的精准度，采用了随机森林来对关键特征进行筛选，同时对未被筛选出的特征进行ER融合。其次，提出了空气质量预测方法的可解释性准则，以规范整个建模过程的可解释性。然后给出了模型的推理和优化方法。最后通过实验验证了该模型的有效性。

Abstract: Air quality has always been one of the top environmental concerns of the public and society, and its quality has been compromised by economic activities. However, due to the complexity of the air and the impact of human activities on it, predicting air quality is not a small challenge. Traditionally, when a single machine learning model is used to predict air quality, it is difficult to obtain good prediction results under various AQI fluctuation trends. In order to solve this problem effectively, a prediction model based on Hierarchy Belief Rule Base-RFI is proposed in this paper, which provides a useful method for air quality prediction. First of all, in order to improve the accuracy of the model, random forest is used to screen the key features, and ER fusion is carried out on the features that are not screened. Secondly, the interpretability criteria of air quality prediction methods are proposed to standardize the interpretability of the whole modeling process. Then the inference and optimization methods of the model are given. Finally, the validity of the model is verified by experiments.