Browsing by Author "Santhana Lakshmi V"

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    AN EXPLORATORY DATA ANALYSIS ON AIR QUALITY DATA OF TRIVANDRUM
    (Springer Link, 2023-05-31) Santhana Lakshmi V; Vijaya M.S
    Data analysis is the most integral part of any research. It is the process of examining the data using statistical methods to identify the hidden patterns and trends which aid in making decisions. This helps in understanding the distribution, correlation, outliers, and missing values found in the data. In this paper, data analysis is performed over the air pollutant data and the meteorological data that influences air pollution. The meteorological data for the period of 4 years of Trivandrum city was taken for the purpose of analysis. The dataset includes 26,544 instances and 23 features. Pollutant parameters such as PM2.5, PM10, CO, SO2, ozone, NOX, and NH3 are considered for analysis. Meteorological features taken for analysis include temperature, dew, humidity, wind speed, wind direction, etc. Meteorological features play a substantial role in identifying air pollution. Boxplots, heat maps, pair plots, and histograms were used to reveal the distribution and correlation between the attributes. From the analysis, it has been identified that the features like sea level pressure, PM2.5, PM10, CO, NOX, NH3, SO2, and ozone are positively correlated with air quality index whereas features like, dew, humidity, wind speed, cloud cover are negatively correlated with air quality index. The results of the data analysis assist in preparing the data for further research.
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    A STUDY ON MACHINE LEARNING-BASED APPROACHES FOR PM2.5 PREDICTION
    (SpringerLink, 2022-01-17) Santhana Lakshmi V; Vijaya M.S
    Clean air and water is the fundamental need of humans. But people are exposed to polluted air produced due to several reasons such as combustion of fossil fuels, industrial discharge, dust and smoke which generates aerosols. Aerosols are tiny droplets or solid particles such as dust and smoke that floats in the atmosphere. The size of the aerosol also called as particulate matter ranges from 0.001–10 μm which when inhaled by human affects the respiratory organs. Air pollution affects the health of 9% of the people every year. It is observed as the most important risk factor that affects human health. There is a need for an efficient mechanism to forecast the quality of air to save the life of the people. Statistical methods and numerical model methods are largely employed for the predicting the value of PM2.5. Machine learning is an application of artificial intelligence that gives a system capability to learn automatically from the data, and hence, it can be applied for the successful prediction of air quality. In this paper, various machine learning methods available to predict the particulate matter 2.5 from time series data are discussed.