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Sustainable optimized LSTM-based intelligent system for air quality prediction in Chennai

Gunasekar Sridhar, Joselin Retna Kumar Gnanaseelan, Dileep Kumar Yellapalli

Acta Geophysica

2022

Vol. 70, no 6

2889--2899

Nowadays, air quality prediction is the most essential process taken by an Indian government. Due to poor quality of air, unhealthy lifestyle and premature deaths of humans have arisen in India, especially in Delhi. Not only has a human’s health, but the air pollution also made a huge impact on several areas like economy, agriculture and road accidents, etc. In recent times, deep learning (DL) technologies are influenced every application rapidly even in air pollution prediction. In this work, the novel optimised DL algorithms are proposed for the efficient prediction of air quality particularly focussing on Chennai, Tamil Nadu. To provide higher accuracy in air quality prediction, the novel optimised DL algorithms are proposed which is combined several models like ARIMA and CNN-LSTM and Tuna Optimization Algorithm, respectively. Initially, CNN and LSTM are combined to provide hybrid architecture. Next, the metaheuristics-based tuna swarm optimization model is applied for fine-tuning the hyperparameters of the CNN-LSTM model which is known as the Tuna Optimised CNN-LSTM (TOCL) method. Finally, the novel TOCL is applied to the residuals of the ARIMA model to form an ARIMA- TOCL (ARTOCL) model. As a result, the novel ARTOCL is learned and performed with an optimal air quality prediction. The metrics of the Hybrid ARTOCL model are evaluated as a better mean absolute error (MAE), root mean squared error (RMSE), R2 score and the normalized RMSE (nRMSE) with higher accuracy than the previous models. The results show that the proposed prediction model has 22.6% R2 improvement, 14.6% MAE reductions, 22% RMSE reductions and 16.45% nRMSE reductions than the existing models.

Data-driven temporal-spatial model for the prediction of AQI in Nanjin

Zhao Xuan, Song Meichen, Liu Anqi, Wang Yiming, Wang Tong, Cao Jinde

Journal of Artificial Intelligence and Soft Computing Research

2020

Vol. 10, No. 4

255--270

Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.