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IoT Enabled Machine Learning for Vehicular Air Pollution Monitoring

机译：物联网支持的机器学习用于车辆空气污染监测

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摘要

Air pollution has become a real-life problem. One of the major pollutant emission is due to the increase in a number of vehicles. Key pollutants generated from the vehicles are Carbon Monoxide (CO), Oxides of Nitrogen (NOx) and Particulate Matter (PM). Conventionally to measure pollution from the road, simulation models were created or various expensive instruments were involved to measure pollution levels which are not likely to be used in real time. Here we have proposed an IOT-based model in which sensors were used to measure CO, PM pollution level and environmental condition like temperature and humidity. The main objective of this approach is to suggest an alternative route to the user based on pollution status and distance of each route which leads to a pollution-free route. The web-based application developed has a Google map API where the pollution status and alternative routes were suggested. With the collected time series samples, the prediction analysis was done for PM with neural network Multi-Layer perceptron and support vector machine regression (SVMR) learning algorithm. With the inferences from the prediction, it is proved that the neural network reduces Mean Absolute Error (MAE) by 27.27 and produces better accuracy when compared with SVMR.
机译：空气污染已经成为现实生活中的问题。主要污染物排放之一是由于车辆数量的增加。车辆产生的主要污染物是一氧化碳（CO），氮氧化物（NOx）和颗粒物（PM）。按照惯例，要测量道路上的污染，会创建模拟模型或使用各种昂贵的工具来测量不太可能实时使用的污染水平。在这里，我们提出了一个基于物联网的模型，其中使用传感器来测量CO，PM污染水平以及温度和湿度等环境条件。这种方法的主要目的是根据污染状况和每条路线的距离向用户建议一条替代路线，从而形成无污染的路线。开发的基于Web的应用程序具有Google Map API，其中建议了污染状况和替代路线。利用收集的时间序列样本，使用神经网络多层感知器和支持向量机回归（SVMR）学习算法对PM进行了预测分析。根据预测的推论，证明了与SVMR相比，神经网络将平均绝对误差（MAE）降低了27.27，并且产生了更好的准确性。

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