Air Quality Predictions in Urban Areas Using Hybrid ARIMA and Metaheuristic LSTM

Gunasekar, S.; Kumar, G. Joselin Retna; Agbulu, G. Pius

doi:10.32604/csse.2022.024303

Cited by 14 publications

(9 citation statements)

References 12 publications

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“…It was suggested that S. Gunasekaret al [15], a novel hybrid AQP model, is built using a Reed deer meta-heuristic optimized LSTM. This network is used to predict air quality.…”

Section: Related Workmentioning

confidence: 99%

Developing the Optimal Hybrid Neural Network for Predicting the Factor of Air Pollutants

Viswanathan

2024

Preprint

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Urban air pollution can be reduced via precise air pollutant forecasts.For that, the air quality index (AQI) quantifies air quality.In this manner, accurate and trustworthy air quality index (AQI) estimates are essential for preserving the natural environment and the general population's health. Using the backpropagation (BP) algorithm, this study describes a method for enhancing the performance of neural networks. Using a network optimized with natural swarm intelligence, a novel optimal-hybrid model approachto Nature Swarm Intelligence (NSI), predicting the Air Quality Index (AQI), is possible. This NSI comprises the optimization algorithms Dove Swarm optimization (DSA) and Bat Algorithm (BA), which aim to optimize the weight of the Backpropagation neural network (BPNN) to promote the air quality prediction. The constructed optimal-hybrid modelcaptured the characteristics of the AQI series and produced a more accurate AQI forecast according to exhaustive comparisons using a set of evaluation indicators. Experiments conducted verify the proposed modelis validfor application when attempting to forecast the AQI. This is because it receives a high RMSE, MAPE, Error Absolute total, and Accuracy value from the simulation. This is because the simulation results suggest that the network model could be a good option for actualization, which is why this is the case.

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“…It was suggested that S. Gunasekaret al [15], a novel hybrid AQP model, is built using a Reed deer meta-heuristic optimized LSTM. This network is used to predict air quality.…”

Section: Related Workmentioning

confidence: 99%

Developing the Optimal Hybrid Neural Network for Predicting the Factor of Air Pollutants

Viswanathan

2024

Preprint

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“…𝑥 𝑖 = 𝑥 − 𝑥 𝑚𝑖𝑛 𝑥𝑚𝑖𝑛 𝑚𝑎𝑥 (22) After model training and prediction, the data need to be subjected to an inverse normalization operation to facilitate the calculation of the evaluation function and plotting, with the inverse normalization equation being as follows. 𝑥 = (𝑥𝑚𝑖𝑛 𝑚𝑎𝑥 𝑥 𝑖 + 𝑥 𝑚𝑖𝑛 ) (23) Among them, 𝑥 𝑖 represents the standardized data, 𝑥 𝑚𝑎𝑥 represents the largest data in the array, and 𝑥 𝑚𝑖𝑛 represents the smallest data in the array.…”

Section: Data Processingmentioning

confidence: 99%

“…[20] combined the efficient features of CNN with the algorithmic advantages of LSTM to propose a CNN-LSTM model to predict future air pollution data. [21] confirmed the regression model by using multiple stepwise regression analysis to find a significant statistical relationship between C6H6 and CO. [22] developed a new hybrid model for air quality prediction, optimizing the residual error of ARIMA by the LSTM algorithm. [23] added an attention mechanism to the model to improve the prediction accuracy of the LSTM model.…”

Section: Introductionmentioning

confidence: 99%

Air-Quality Prediction Based on the ARIMA-CNN- LSTM Combination Model optimized by Dung Beetle Optimizer

Duan

Gong

Luo

et al. 2023

Preprint

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The air pollution problem seriously affects economic development and people's health, and an efficient and accurate forecasting model of air quality will help manage air pollution problems. The comparison models chosen by other scholars are often based on derivative models of the proposed model and do not comprehensively compare other types of models with limited accuracy. In this paper, we establishes a combined ARIMA-CNN-LSTM model to predict the air quality index accurately. The model mainly consists of two parts: Using the ARIMA model to fit the linear part of the data and using the CNN-LSTM model to fit the nonlinear part of the data to avoid the problem of blindness in the CNN-LSTM hyperparameter setting. To avoid the dilemma of blindness in a CNN-LSTM hyperparameter setting, this article uses the Dung Beetle Optimizer tool to find the hyperparameters of a CNN-LSTM model, determine the best hyperparameters, and check the accuracy of the model. The proposed model is compared with other widely used models. The results show that the Dung Beetle Optimizer can effectively search for the optimal hyperparameters of the model and can solve the problem of blindness in setting the hyperparameters of the model. And the optimized ARIMA-DBO-CNN-LSTM model has higher predictive accuracy with stronger adaptability in predicting the three cities.

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“…This field covers numerous variants of Neural network (NN), Support Vector Machine (SVM), decision trees, Random forest and many others. Results in this field are published in [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Paper [11] applies a wavelet model to decompose meteorological data to improve feature selection and designs a radial basis function long short-term memory (LSTM) model for modeling PM2.5 pollution data. Authors of [12] develop a new hybrid model for air quality prediction based on a reed deer metaheuristic optimized LSTM Deep Learning network coupled with ARIMA models. Sánchez et al [13] combine SVM regression and multilayer perceptron (MLP) to model nitrogen oxides (NOx), CO, SO2, O3 and PM10.…”

Section: Introductionmentioning

confidence: 99%

Temporal-causal modeling of air pollution in the city of Plovdiv, Bulgaria: a case study

Ivanov,

Gocheva-Ilieva,

Stoimenova-Minova

2023

J. Phys.: Conf. Ser.

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High levels of air pollution are dangerous to human health, which is a current problem for densely populated cities worldwide. Studying this problem can help detect pollutants’ time dependencies on basic meteorological measurements and other factors for future prediction and elaborate corresponding alarms when official upper pollution limits are exceeded. In this work, time-causal models based on previous daily time observations and meteorological measurements in the city of Plovdiv, Bulgaria, are applied. Vector-type temporal-causal models are constructed and analyzed for carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine dust particles below size 10, 2.5, and 1 micron (PM10, PM2.5, and PM1), respectively. Pollution levels are predicted seven days ahead.

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Air Quality Predictions in Urban Areas Using Hybrid ARIMA and Metaheuristic LSTM

Cited by 14 publications

References 12 publications

Developing the Optimal Hybrid Neural Network for Predicting the Factor of Air Pollutants

Developing the Optimal Hybrid Neural Network for Predicting the Factor of Air Pollutants

Air-Quality Prediction Based on the ARIMA-CNN- LSTM Combination Model optimized by Dung Beetle Optimizer

Temporal-causal modeling of air pollution in the city of Plovdiv, Bulgaria: a case study

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