Abstract
Air Pollution is one of the major issues concerning the entire world. There are many pollutants in the atmosphere which cause the degradation of air leading to a harmful environment. This work presents the analysis of such pollutants and to predict them using the Auto Regressive Integrated Moving Average (ARIMA) model. ARIMA model is one of the time series analysis model which gives the prediction of certain values based on the historical data. The data set used in this model contains of various pollutants values observed on a specific date in a particular location. ARIMA model when applied on the data set resulted in the prediction of the pollutants. It is an efficient way by which we can find out whether the values of the pollutants are exceeding the limits prescribed by the World Health Organization (WHO). Thus it creates awareness among people and government so that certain actions can be taken to decrease the levels of such harmful pollutants. The effectiveness of this technique is investigated on the available data set and its performance is measured.
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References
Lin WY, Hsiao MC, Wu PC, Fu JS, Lai LW, Lai HC (2020) Analysis of air quality and health co-benefits regarding electric vehicle promotion coupled with power plant emissions. J Cleaner Prod 247:119152
Beig G, Sahu SK, Singh V, Tikle S, Sobhana SB, Gargeva P, Ramakrishna K, Rathod A, Murthy BS (2020) Objective evaluation of stubble emission of North India and quantifying its impact on air quality of Delhi. Sci Total Environ 709:136126
Koo JW, Wong SW, Selvachandran G, Long HV (2020) Prediction of air pollution index in Kuala Lumpur using fuzzy time series and statistical models. Air Qual Atmos Health, pp 1-12
Samal KKR, Babu KS, Das SK, Acharaya A (2019) Time series based air pollution forecasting using SARIMA and prophet model. In: Proceedings of the 2019 international conference on information technology and computer communications, pp 80-85
Mudholkar A, Akash S, Ajay C, Gowramma GS (2019) Air pollution data analysis using ARIMA model
Pant P, Lal RM, Guttikunda SK, Russell AG, Nagpure AS, Ramaswami A, Peltier RE (2019) Monitoring particulate matter in India: recent trends and future outlook. Air Qual Atmos Health 12(1):45–58
Jain S, Mandowara V (2019) Study on particulate matter pollution in jaipur city. Int J Appl Eng Res 14(3):637–645
Jauvion G, Cassard T, Quennehen B, Lissmyr D (2020) DeepPlume: very high resolution real-time air quality mapping
Singh T, Narasimhan TL, Lakshminarayanan CS (2020) DeepAir: air quality prediction using deep neural network. In: TENCON IEEE region 10 conference (TENCON). IEEE
Bhardwaj R, Pruthi D (2020) Development of model for sustainable nitrogen dioxide prediction using neuronal networks. Int J Environ Sci Technol, pp 1–10
Shah J, Mishra B (2020) Analytical equations based prediction approach for PM2. 5 using artificial neural network
Saha D, Soni K, Mohanan MN, Singh M (2019) Long-term trend of ventilation coefficient over Delhi and its potential impacts on air quality. Rem Sens Appl Soc Environ 15:100234
Geetha A, Nasira GM (2016) Time series modeling and forecasting: tropical cyclone prediction using ARIMA model. In: 2016 3rd International conference on computing for sustainable global development (INDIACom). IEEE
Bose R, Dey RK, Roy S, Sarddar D (2020) Time series forecasting using double exponential smoothing for predicting the major ambient air pollutants. Information and communication technology for sustainable development. Springer, Singapore, pp 603–613
Adebiyi AA, Adewumi AO, Ayo CK (2014) Comparison of ARIMA and artificial neural networks models for stock price prediction
Fatima S, Ali SS, Zia SS, Hussain E, Fraz TR, Khan MS (2019) Forecasting carbon dioxide emission of Asian countries using ARIMA and simple exponential smoothing models. Int J Econ Environ Geol 10(1):64–69
Mustafa S, Siddique S, Kadilar C (2020) Improved fuzzy forecasting model for stock exchange market. Punjab Univ J Math 52(1):1–5
Musa Y, Stephen J (2020) Analysis of ARIMA-artificial neural network hybrid model in forecasting of stock market returns. Asian J Prob Stat, pp 42-53
Data set [URL] https://www.kaggle.com/shrutibhargava94/india-air-quality-data#data.csv. Last accessed on 15 Mar 2020
Tewari A (2020) Forecasting NIFTY 50 benchmark index using seasonal ARIMA time series models
Dhoot R, Saumay A, Shushil Kumar M (2019) Implementation and analysis of arima model and kalman filter for weather forcasting in spark computing environment. In: 2019 3rd International conference on computing and communications technologies (ICCCT). IEEE
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Gopu, P., Panda, R.R., Nagwani, N.K. (2021). Time Series Analysis Using ARIMA Model for Air Pollution Prediction in Hyderabad City of India. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 1325. Springer, Singapore. https://doi.org/10.1007/978-981-33-6912-2_5
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DOI: https://doi.org/10.1007/978-981-33-6912-2_5
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