Air pollution has become an international calamity, a problem for human health and the environment. The ability to predict the air quality becomes a crucial task. The usual approaches for assessing air quality are exhausted when extracting complicated non-linear relationships and long-term dependence features embedded in the data. Long- and short-term memory, a recurrent neural network family, has emerged as a potent tool for addressing the mentioned issues, so computer-aided technology has become essential to aid with a high level of prediction and best-in-class accuracy. In this study, we investigated classic time-series analysis based on Improved Long short-term memory (ILSTM) to improve the performance of air quality index prediction. The predicted AQI value for the 25 days lies in a 97.63% Confidence interval zone and highly adoptable performance metrics such as R-Square, MSE, RMSE, and MAE values.
SarmadiMRahimiSRezaeiMSanaeiDDianatinasabM. Air quality index variation before and after the onset of COVID-19 pandemic: a comprehensive study on 87 capital, industrial and polluted cities of the world. Environmental Sciences Europe. 2021; 33(134): 1–17.
2.
XayasoukTLeeHLeeG. Air pollution prediction using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models. Sustainability. 2020; 12(6).
3.
KimYBParkSBLeeSParkYK. Comparison of PM2.5 prediction performance of the three deep learning models: A case study of Seoul, Daejeon, and Busan. Journal of Industrial and Engineering Chemistry. 2023; 120: 159–69.
4.
ShriramPMalladiS. A Study and Analysis of Air Quality Index and Related Health Impact on Public Health. In: International Conference on IoT based Control Networks and Intelligent Systems (ICICNIS 2020). SSRN. 2020; 149–61.
5.
YasminFHassanMMHasanMZamanSAngonJHBairagiAK, et al. AQIPred: A hybrid model for high precision time specifc forecasting of air quality index with cluster analysis. Human-Centric Intelligent Systems. 2023; 3: 275–95.
6.
El-MagdSASolimanGMorsyMKharbishS. Environmental hazard assessment and monitoring for air pollution using machine learning and remote sensing. International Journal of Environmental Science and Technology. 2023; 20: 6103–16.
7.
KapadiaDJariwalaN. Prediction of tropospheric ozone using artificial neural network (ANN) and feature selection techniques. Modeling Earth Systems and Environment. 2022; 8: 2183–92.
8.
FakherHAAhmedZAcheampongAONathanielSP. Renewable energy, nonrenewable energy, and environmental quality nexus: An investigation of the N-shaped Environmental Kuznets Curve based on six environmental indicators. Energy. 2023; 263: 125660.
9.
ZhangZZhangGLiL. The spatial impact of atmospheric environmental policy on public health based on the mediation effect of air pollution in China. Artificial Intelligence Review. 2023; 30(55): 116584–600.
10.
HandhayaniT. An integrated analysis of air pollution and meteorological conditions in Jakarta. Scientific Reports. 2023; 13(5798): 1–11.
11.
XiaoFYangMFanHFanGAl-qanessMAA. An improved deep learning model for predicting daily PM2.5. concentration. Scientifc Reports. 2020; 10: 20988.
12.
LaTVDaoMSTejimaKKiranRUZettsuK. Improving the awareness of sustainable smart cities by analyzing lifelog images and IoT air pollution data. In: 2021 IEEE International Conference on Big Data (Big Data). 2021; 3589–94.
13.
JanarthananRPartheebanPSomasundaramKElamparithiPN. A deep learning approach for prediction of air quality index in a metropolitan city. Sustainable Cities and Society. 2021; 67: 102720.
14.
AmeerSShahMAKhanASongHMapleCIslamSU, et al. Comparative analysis of machine learning techniques for predicting air quality in smart cities. IEEE Access. 2019; 7: 128325–38.
15.
ZengYChenJJinNJinXDuY. Air quality forecasting with hybrid LSTM and extended stationary wavelet transform. Building and Environment. 2022; 213: 108822.
16.
YuMXuFHuWSunJCervoneG. Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for localized surface temperature forecasting in an urban environment. IEEE Access. 2021; 9: 137406–18.
17.
KimBKimEJungSKimMKimJKimS. PM2.5 concentration forecasting using weighted Bi-LSTM and random forest feature importance-based feature selection. Atmosphere. 2023; 14(6).
18.
XuRWangDLiJWanHShenSGuoX. A hybrid deep learning model for air quality prediction based on the time-frequency domain relationship. Atmosphere. 2023; 14(2).
19.
LiTHuaMWuX. A hybrid CNN-LSTM model for forecasting particulate matter (PM2.5). IEEE Access. 2020; 8: 26933–40.
20.
SuYLiJLiuLGuoXHuangLHuM. Application of CNN-LSTM algorithm for PM2.5 concentration forecasting in the Beijing-Tianjin-Hebei metropolitan area. Atmosphere. 2023; 14(9).
21.
TranDHuangHYYuJYWangSH. Forecasting hourly PM2.5 concentration with an optimized LSTM model. Atmospheric Environment. 2023; 10(315): 120161.
22.
Suman. Air quality indices: A review of methods to interpret air quality status. Materials Today: Proceedings. 2021; 34: 863–8.
23.
WangJLiXJinLLiJSunQWangH. An air quality index prediction model based on CNN-ILSTM. Scientific Reports. 2022; 12(8373): 1–16.
24.
CastelliMClementeFMPopovicASilvaSVanneschiL. A machine learning approach to predict air quality in california. Complexity. 2020; 2020(Article ID 8049504): 1–20.
25.
ManiGViswanadhapalliJKStonierAA. Prediction and forecasting of air quality index in Chennai using regression and ARIMA time series models. Journal of Engineering Research. 2021; 10(2A): 179–94.
26.
LiuHLiQYuDGuY. Air quality index and air pollutant concentration prediction based on machine learning algorithms. Applied Sciences. 2019; 9(19): 1–9.
27.
KumarRKumarPKumarY. Time series data prediction using IoT and machine learning technique. Procedia Computer Science. 2020; 167: 373–81. International Conference on Computational Intelligence and Data Science.
28.
MalekiHSorooshianAGoudarziGBaboliZBirganiYTRahmatiM. Air pollution prediction by using an artificial neural network model. Clean Technologies and Environmental Policy. 2019; 21: 1341–52.
29.
ZhaoXSongMLiuAWangYWangTCaoJ. Data-driven temporal-spatial model for the prediction of AQI in Nanjing. Journal of Artificial Intelligence and Soft Computing Research. 2020; 10(4): 255–70.
30.
SinghSSinghJGoyalSBSehraSSAliFAlkhafajiMA, et al. A novel framework to avoid traffic congestion and air pollution for sustainable development of smart cities. Sustainable Energy Technologies and Assessments. 2023; 56: 103125.
31.
CaglarAEAskinBE. A path towards green revolution: How do competitive industrial performance and renewable energy consumption influence environmental quality indicators? Renewable Energy. 2023; 205: 273–80.
32.
ZhangQWuSWangXSunBLiuH. A PM2.5 concentration prediction model based on multi-task deep learning for intensive air quality monitoring stations. Journal of Cleaner Production. 2020; 275: 122722.
33.
YangJIsmailALiYZhangLFadzliFE. Transfer learning-driven hourly PM2.5 prediction based on a modified hybrid deep learning. IEEE Access. 2023; 11: 99614–27.
34.
DrewilGIAl-BahadiliRJ. Air pollution prediction using LSTM deep learning and metaheuristics algorithms. Measurement: Sensors. 2022; 24: 100546.
35.
ChowdhuryASUddinMSTanjimMRNoorFRahmanRM. Application of data mining techniques on air pollution of dhaka city. In: 2020 IEEE 10th International Conference on Intelligent Systems (IS). 2020; 562–7.
36.
RenYWangSXiaB. Deep learning coupled model based on TCN-LSTM for particulate matter concentration prediction. Atmospheric Pollution Research. 2023; 14(4): 101703.
37.
MendezMMerayoMGNunezM. Machine learning algorithms to forecast air quality: A survey. Artificial Intelligence Review. 2023; 56: 10031–66.
38.
KumarKPandeBP. Air pollution prediction with machine learning: a case study of Indian cities. International Journal of Environmental Science and Technology. 2023; 20: 5333–48.
39.
ZhangGDavoodiSBandSSGhorbaniHMosaviAMoslehpourM. A robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques. Energy Reports. 2022; 8: 2233–47.
40.
FanSHaoDFengYXiaKYangW. A hybrid model for air quality prediction based on data decomposition. Information. 2021; 12(5): 1–12.
41.
DuanJGongYLuoJZhaoZ. Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer. Scientific Reports. 2023; 13(12127): 1–16.
42.
BarthwalAAcharyaDLohaniD. Prediction and analysis of particulate matter (PM2.5 and PM10) concentrations using machine learning techniques. Journal of Ambient Intelligence and Humanized Computing. 2023; 14: 1323–38.
43.
JinXBWangZYKongJLBaiYTSuTLMaHJ, et al. Deep spatio-temporal graph network with self-optimization for air quality prediction. Entropy. 2023; 25(2).
44.
LuoJGongY. Air pollutant prediction based on ARIMA-WOA-LSTM model. Atmospheric Pollution Research. 2023; 14(6): 101761.
45.
YangJIsmailAWLiYZhangLFadzliFE. Transfer learning-driven hourly PM2.5 prediction based on a modified hybrid deep learning. IEEE Access. 2023; 11: 99614–27.
46.
RakholiaRLeQHoBQVuKCarbajoRS. Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam. Environment International. 2023; 173: 107848.
