德里空气污染预测:使用时间序列预测的比较分析

Sparsh Singh, Vidit Kumar, Zaid K. Ahmed, Kajol Mittal
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引用次数: 0

摘要

近年来,由于空气污染的负面影响,对空气污染的研究大幅增加,它也被认为是当前大气中的一个。这是主要的风险因素之一。准确的空气质量评估是实施空气污染控制系统的第一步,这有助于发达国家的经济和社会发展。这两种系统准确的空气质量预测对排放控制、公众健康和福祉至关重要。在过去的两年里,印度的大都市德里一直是世界上污染最严重的大都市。在本研究中,使用SARIMAX、Prophet和LSTM这三种不同的机器学习算法对结果进行了比较。所有模型都采用了相同数量的参数校准,SARIMAX似乎是三种模型中更可靠的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Delhi Air Pollution Prediction: A Comparative Analysis using Time Series Forecasting
Recent years have seen a substantial increase in study on air pollution as a result of its negative ramifications It is also acknowledged as one in the current atmosphere. one of the main risk elements. Accurate air quality assessment is the first stage in the implementation of air pollution control systems, which helps in the growth of developed nations' economies and societies. the two methodical Accurate air quality predictions are essential for emissions control, public health, and wellbeing. The metropolis of India, Delhi, has been the most polluted metropolis in the world for the past two years. In this study, the results have been compared using SARIMAX, Prophet, and LSTM are three distinct machine learning algorithms that were tested against one another. The same quantity of parameter calibration was applied to all the models, and SARIMAX seems to be more reliable out of the three.
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