利用机器学习进行空气质量预测

Pankaj Singh, Yashashwini R, Srinidhi Kulkarni, Saravana M K
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引用次数: 0

摘要

在本文中,它旨在通过使用机器学习算法预测空气质量指数(AQI)。为此,选取影响空气质量指数的关键参数分别为温度、湿度、压力、风速、PM10和SO2。印度某些邦的空气质量可以作为决定污染指数的主要因素之一,也可以作为城市工业和人口控制程度的主要因素之一。随着工业化的到来,城市化空气质量监测一直是一个挑战。空气污染对环境和人类健康造成了明显的损害,导致酸雨、心脏病、全球变暖和全人类的皮肤癌。本文解决了预测空气质量指数(AQI)的挑战,目标是在污染变得不利之前减少污染,并建议人类提前搬家,使用集合技术预测空气质量指数(AQI)。本文基于印度的污染和气象信息,研究了一些可用的预测模型在提供一些输入数据的情况下预测空气质量指数(AQI)值的有效性。我们对数据集进行回归分析,结果显示了气象因子对空气质量指数的影响最大,以及预测模型对空气质量预测的帮助程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AIR QUALITY PREDICTION USING MACHINE LEARNING
In this paper, it is aimed to predict the Air Quality Index (AQI) by the use of Machine learning algorithms. To reach this, the key parameters have been selected which can affect the Air quality index are temperature, humidity, pressure, wind speed, PM10 and SO2 respectively. Air quality of certain states in India can be used as one of the major factors determining pollution index also how well the city's industries and population is controlled. Urbanized Air quality monitoring has been a constant challenge with the advent of industrialization. Air pollution causes conspicuous damage to the environment as well as to human health resulting in acid rain, heart diseases, global warming and skin cancer to all humankind. This paper addresses the challenge of predicting the Air Quality Index (AQI), with the goal to reduce the pollution before it gets unfavourable and also suggests mankind to move places in advance, using ensemble techniques for predicting the Air Quality Index (AQI). This paper investigates how effective some available prediction models are in predicting the Air Quality Index (AQI) values provided some input data, based on the pollution and meteorological information in India. We carry out regression analysis on the dataset, and our results shows which meteorological factors impact the AQI values most and how helpful the predictive models are to help in air quality prediction.
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