Time Series Analysis Using ARIMA Model for Air Pollution Prediction in Hyderabad City of India

G, Pooja; Panda, Rama Ranjan; Nagwani, Naresh Kumar

doi:10.1007/978-981-33-6912-2_5

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…All baseline models were trained and tested using identical training, validation, and testing time-series datasets. Detailed information regarding the selected baseline models is provided below: ARIMA [ 20 ]: Autoregressive integrated moving average model, comprehensively used as an interpretable statistical model for time-series forecasting. SVR [ 48 ]: Support vector regression model, a machine learning model that utilizes support vectors for regression tasks.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Conventional methods for air quality prediction primarily comprise interpretable statistical methods and machine learning models. Statistical methods, fundamentally grounded in mathematically interpretable models such as vector autoregressive (VAR) models [ 19 ] and autoregressive integrated moving average (ARIMA) [ 20 ], impose stringent requirements on input data, often necessitating data that pass stationarity tests. In contrast, machine learning methods do not demand specific input data prerequisites and adeptly address nonlinear fitting challenges.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

Xia,

Chen,

Wang

et al. 2024

Entropy

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The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction’s mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

Xia,

Chen,

Wang

et al. 2024

Entropy

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“…Time series analysis using the ADF test generates statistics (scores) as shown in equation 1 (∆[PM 2.5 ] t ). By comparing these statistics with thresholds, it is possible to determine whether PM2.5 concentrations have a unit root that indicates nonstationarity [13]. If the calculated ADF test statistic is less than the critical value corresponding to the significance level (1%, 5%, 10%), the null hypothesis is rejected, concluding that the time series of PM2.5 concentrations is stationary [14].…”

Section: Development and Validation Of The Data Assimilation Modelmentioning

confidence: 99%

Enhancing daily PM2.5 air quality forecasts in Dakar, Senegal, through the integration of data from the automatic measuring station into a server using data assimilation

gueye,

Drame,

Niang

et al. 2024

Preprint

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The objective of this work is to predict daily PM2.5 air quality in Dakar, Senegal using data from an automated measurement station integrated into a server using a data assimilation model. Initially, a 3-year data set was used to identify and validate an appropriate ARIMA data assimilation model. The data was split into an 80% training set and a 20% test set. The Augmented Dickey-Fuller (ADF) test was used to check the normality of the data series. Subsequently, we used the AutoArima method to determine the optimal model to represent the time series. Preliminary results show that a model with order (2,1,1) accurately represents the series. Additional analysis using model fit tests showed that the (3, 0, 1) model was most effective in representing and predicting the data. The statistical validation performance of this model demonstrates its capability to forecast PM2.5 concentrations for up to 72 hours (3 days), achieving correlation coefficients exceeding 80%. However, after three days, the predictions returned to background levels. In the final stage of the study, data from automatic stations were integrated into a server hosting the assimilation model to improve daily PM2.5 forecasts for Dakar. An interactive platform was developed to visualize measurements and forecasts over two days. The results show that by integrating the data with the assimilation model, predictions are significantly improved.

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“…ARIMA model has also been used in the analysis of air pollutants and to predict them based on historical data in (Gopu et al, 2021) and also said that this is an efficient way by which we can find out the values of the pollutants when exceeding the limits prescribed by the World Health Organization (WHO). SARIMA is an ARIMA capable of dealing with the seasonality of the dataset, and SARIMAX is SARIMA with X factor, which is nothing but exogenous factors that affect the data.…”

Section: Related Workmentioning

confidence: 99%

Machine learning-based time series models for effective CO2 emission prediction in India

Kumari

Singh

2022

Environ Sci Pollut Res

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As per the report of datacommons.org, 𝐶𝑂 2 emission in India is 1.80 metric tons per capita, which is very harmful for living beings. So, this paper presents India's detrimental 𝐶𝑂 2 emission effect with the prediction of 𝐶𝑂 2 emission for the next ten years based on univariate time series data. We used three statistical models i.e., AutoRegressive Integrated Moving Average (ARIMA) model, Seasonal AutoRegressive Integrated Moving Average with eXogenous factors (SARIMAX) model, and Holt-Winter model, two machine learning models, i.e., Linear Regression and Random Forest model and one deep learning model, i.e. Long Short Term Memory (LSTM) model. The performance analysis shows that LSTM, SARIMAX, and Holt-Winter methods are accurate models and have the Least Mean Squared Error(MSE), Root Mean Square Error (RMSE), and Median Absolute Error (MedAE) for this kind of univariate data distribution.

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Time Series Analysis Using ARIMA Model for Air Pollution Prediction in Hyderabad City of India

Cited by 13 publications

References 14 publications

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

Enhancing daily PM2.5 air quality forecasts in Dakar, Senegal, through the integration of data from the automatic measuring station into a server using data assimilation

Machine learning-based time series models for effective CO2 emission prediction in India

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