Time series analysis of pm10 for Noida Sector 1 Industrial Area in NCR using multiple linear regression

Kumar, Gaurav

doi:10.5958/2320-3226.2018.00028.0

Cited by 2 publications

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“…The Indian Air Pollution Control Board keeps an eye on PM10, NO2, and SO2 levels. PM10 is the most hazardous of these three since it is the tiniest (Kumar, 2018). The government can use time series analysis to help create a plan to lower PM10.…”

Section: Introductionmentioning

confidence: 99%

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A Time Series Study of PM10 Pollutant in Ghaziabad City Using Multiple Linear Regression

kumar,

Kumar

2024

Preprint

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Air pollution has grown to be a serious issue in Ghaziabad, Uttar Pradesh. PM10, SO2, and NO2 are a few examples of air pollution constituents. The forecasting of these pollutants can help in the formulation of a strategy to decrease air pollution. This study analyzes the air toxin PM10 for the Khora Colony of Ghaziabad, Uttar Pradesh, utilizing data from previous years produced by the Uttar Pradesh Pollution Control Board. Multiple linear regression (MLR) is used for the analysis.

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Section: Introductionmentioning

confidence: 99%

“…This may be used to environmental economics and nancial forecasts, although it is most commonly utilized in scienti c domains like signal processing and statistics. Time series analysis may be done using a variety of methods, such as the moving average approach, regression, the ANN method, and more (Kumar 2018).…”

Section: Introductionmentioning

confidence: 99%

A Time Series Study of PM10 Pollutant in Ghaziabad City Using Multiple Linear Regression

kumar,

Kumar

2024

Preprint

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Analysis and Prediction of Subway Ridership

Liu¹

2023

HSET

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Many people go by subway in China. Huge passenger flow brings much trouble to the police and passengers, such as crowded carriages, long waiting times and low-efficiency transport. So it is important to know the traffic jam before it brings many problems. With the data from Tianchi Competition, this paper analyzes and predicts the subway ridership of a station in Hangzhou based on time series and linear regression. Taking line 3, station 5 as an example, there were two peaks which have many passengers in the station. Combined with the results, it proposed that traffic police need to pay more attention to the rush hours. People who do not need to commute can avoid these times. The government may also use this prediction results in the subway service management, as well as in the planning for the future development and subway lines projection.

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Time series analysis of pm10 for Noida Sector 1 Industrial Area in NCR using multiple linear regression

Cited by 2 publications

References 0 publications

A Time Series Study of PM10 Pollutant in Ghaziabad City Using Multiple Linear Regression

A Time Series Study of PM10 Pollutant in Ghaziabad City Using Multiple Linear Regression

Analysis and Prediction of Subway Ridership

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