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首页> 外文期刊>Aerosol and Air Quality Research >Hybridization of Air Quality Forecasting Models Using Machine Learning and Clustering: An Original Approach to Detect Pollutant Peaks
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Hybridization of Air Quality Forecasting Models Using Machine Learning and Clustering: An Original Approach to Detect Pollutant Peaks

机译：基于机器学习和聚类的空气质量预测模型的混合：检测污染物峰值的原始方法

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This paper presents an original approach combining Artificial Neural Networks (ANNs) and clustering in order to detect pollutant peaks. We developed air quality forecasting models using machine learning methods applied to hourly concentrations of ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10) 24 hours ahead. MultiLayer Perceptron (MLP) was used alone, then hybridized successively with hierarchical clustering and with a combination of self-organizing map and k-means clustering. Clustering methods were used to subdivide the dataset, and then an MLP was trained on each subset. Two urban sites of Corsica Island in the western Mediterranean Sea were investigated. These models showed a good global precision (Index of Agreement reaching 0.87 for O3, 0.80 for NO2 and 0.74 for PM10). Considering it is particularly important than forecasting model used on an operational basis correctly predict pollution peaks, a sensitivity analysis was performed using Receiver Operating Characteristic curves (ROC curves). It allowed to evaluate the behaviour and the robustness of the models for high concentration situations. The results show that for PM10 and O3, hybrid models made of a combination of clustering and MLP outperform classical MLP most of the time for high concentration prediction. An operational tool has been built with the models presented in this paper, and is used for air quality forecasting in Corsica.
机译：本文提出了一种结合人工神经网络（ANN）和聚类的原始方法来检测污染物峰值。我们使用机器学习方法开发了空气质量预测模型，该模型适用于24小时前每小时的臭氧（O3），二氧化氮（NO2）和颗粒物（PM10）浓度。多层感知器（MLP）可以单独使用，然后与层次聚类以及自组织图和k均值聚类相结合依次杂交。使用聚类方法细分数据集，然后在每个子集上训练一个MLP。调查了地中海西部科西嘉岛的两个城市地点。这些模型显示出良好的全局精度（O3的协议指数达到0.87，NO2的协议指数达到0.80，PM10的协议指数达到0.74）。考虑到比在运行基础上使用的预测模型正确预测污染峰特别重要，使用接收器运行特征曲线（ROC曲线）进行了灵敏度分析。它允许评估高浓度情况下模型的行为和鲁棒性。结果表明，对于PM10和O3，由聚类和MLP组合而成的混合模型在大部分时间进行高浓度预测时都优于经典MLP。已使用本文介绍的模型构建了一种操作工具，并将其用于科西嘉岛的空气质量预测。

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