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@article{154171,
        author = {R.Udaya Shanmuga and Dr.G.TamilPavai},
        title = {AUTOMATED AIR POLLUTION DETECTION AND ESTIMATION OF AQI USING ResNET},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {39-48},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154171},
        abstract = {Air  Pollution  is  turning  the  whole  world
prematurely grey. The health effects of air pollution imperil human lives especially for at-risk population and those with respiratory illness and the fact is well- documented. Many researches proposed to estimate particulate matter values from smartphone images, given  that  deploying highly accurate air  pollution monitors  throughout a  city  is  a  highly  expensive. Departing from previous machine learning studies which primarily focus on pollutant estimation based on single day-time images, our proposed deep learning model integrates Residual Network (ResNet) with Long Short-Term Memory (LSTM), extracting spatial-temporal features of sequential images taken from smartphones instead for estimating PM2.5 and PM10 values of a particular location at a particular time. Our methodology is as follows: First, images are obtained constantly with regular time intervals. Second, verified experimentally that any PM2.5 and PM10 values obtained remain constant within a radius of 500 meters. Third, the proposed ResNet-LSTM was constructed and extended by incorporating meteorological information and one short path. In future, our deep-learning image-based air pollution estimation study will incorporate sequential images obtained from 24-hr operating traffic surveillance cameras distributed across all parts to provide full- day and more fine-grained image-based air pollution estimation for the city.
},
        keywords = {},
        month = {},
        }