A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic

Zhao, Zixi; Wu, Jinran; Cai, Fengjing; Zhang, Shaotong; Wang, You‐Gan

doi:10.1038/s41598-023-28287-8

Cited by 35 publications

(10 citation statements)

References 65 publications

(72 reference statements)

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“…Parameter q 0 i is the initial quarantine rate, parameter q mi denotes the maximum quarantine rate with the intervention being implemented, and parameter r 2 i represents the exponential increasing rate of quarantine rate, i = 1, 2, 3. In contrast to the exponential decay/increasing functions of c 1 ( t ) and q 1 ( t ), the sustained strengthening control strategies is described by the Gaussian decay functions [ 51 ] of c 2 ( t ) and q 2 ( t ). Additionally, the construction of c 3 ( t ) and q 3 ( t ) is based on the analytical solution of the Rosenzweig model [ 52 ], where m and n are interference constants.…”

Section: Resultsmentioning

confidence: 99%

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

He,

Tang,

Xiao

2023

PLoS Comput Biol

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During the COVID-19 pandemic, control measures, especially massive contact tracing following prompt quarantine and isolation, play an important role in mitigating the disease spread, and quantifying the dynamic contact rate and quarantine rate and estimate their impacts remain challenging. To precisely quantify the intensity of interventions, we develop the mechanism of physics-informed neural network (PINN) to propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining scattered observational data with deep learning and epidemic models. The TDINN algorithm can not only avoid assuming the specific rate functions in advance but also make neural networks follow the rules of epidemic systems in the process of learning. We show that the proposed algorithm can fit the multi-source epidemic data in Xi’an, Guangzhou and Yangzhou cities well, and moreover reconstruct the epidemic development trend in Hainan and Xinjiang with incomplete reported data. We inferred the temporal evolution patterns of contact/quarantine rates, selected the best combination from the family of functions to accurately simulate the contact/quarantine time series learned by TDINN algorithm, and consequently reconstructed the epidemic process. The selected rate functions based on the time series inferred by deep learning have epidemiologically reasonable meanings. In addition, the proposed TDINN algorithm has also been verified by COVID-19 epidemic data with multiple waves in Liaoning province and shows good performance. We find the significant fluctuations in estimated contact/quarantine rates, and a feedback loop between the strengthening/relaxation of intervention strategies and the recurrence of the outbreaks. Moreover, the findings show that there is diversity in the shape of the temporal evolution curves of the inferred contact/quarantine rates in the considered regions, which indicates variation in the intensity of control strategies adopted in various regions.

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Section: Resultsmentioning

confidence: 99%

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

He,

Tang,

Xiao

2023

PLoS Comput Biol

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“…Zhao et al [16] extended the analysis beyond pollutants and meteorological factors to encompass social factors, such as the implementation of lockdown policies during the COVID-19 pandemic, as dependent variables in predicting the Air Quality Index (AQI). Multiple linear regression was employed to mitigate the influence of seasonal and epidemic factors on the original series, thereby facilitating the extraction of potential information from the dataset.…”

Section: Related Workmentioning

confidence: 99%

A Time Series Approach to Smart City Transformation: The Problem of Air Pollution in Brescia

Pagano,

Barbierato

2023

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Air pollution is a paramount issue, influenced by a combination of natural and anthropogenic sources, various diffusion modes, and profound repercussions for the environment and human health. Herein, the power of time series data becomes evident, as it proves indispensable for capturing pollutant concentrations over time. These data unveil critical insights, including trends, seasonal and cyclical patterns, and the crucial property of stationarity. Brescia, a town located in Northern Italy, faces the pressing challenge of air pollution. To enhance its status as a smart city and address this concern effectively, statistical methods employed in time series analysis play a pivotal role. This article is dedicated to examining how ARIMA and LSTM models can empower Brescia as a smart city by fitting and forecasting specific pollution forms. These models have established themselves as effective tools for predicting future pollution levels. Notably, the intricate nature of the phenomena becomes apparent through the high variability of particulate matter. Even during extraordinary events like the COVID-19 lockdown, where substantial reductions in emissions were observed, the analysis revealed that this reduction did not proportionally decrease PM2.5 and PM10 concentrations. This underscores the complex nature of the issue and the need for advanced data-driven solutions to make Brescia a truly smart city.

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“…Therefore, during the epidemic period, whether in the economic field or social field, many linear or machine learning prediction model-related research Generated in large quantities, for example: Wu et al ( 2022 ) once used a time series prediction model to predict half-hourly electricity demand in Victoria. Zhao et al ( 2023 ) once constructed a deep learning framework, combining time autocorrelation with Spatially correlated combination, reflecting the impact of neighboring cities and historical data on air quality during COVID-19. Cui et al ( 2023 ) propose a deep learning framework with a COVID-19 adjustment for electricity demand forecasting.…”

Section: Introductionmentioning

confidence: 99%

Application of hybrid fuzzy interval-based machine learning models on financial time series — A case study of Taiwan biotech index during the epidemic period

Lin,

Hsu

2024

Front. Artif. Intell.

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In recent years, the use of machine learning to predict stock market indices has emerged as a vital concern in the FinTech domain. However, the inherent nature of point estimation in traditional supervised machine learning models leads to an almost negligible probability of achieving perfect predictions, significantly constraining the applicability of machine learning prediction models. This study employs 4 machine learning models, namely BPN, LSTM, RF, and ELM, to establish predictive models for the Taiwan biotech index during the COVID-19 period. Additionally, it integrates the Gaussian membership function MF from fuzzy theory to develop 4 hybrid fuzzy interval-based machine learning models, evaluating their predictive accuracy through empirical analysis and comparing them with conventional point estimation models. The empirical data is sourced from the financial time series of the “M1722 Listed Biotechnology and Medical Care Index” compiled by the Taiwan Economic Journal during the outbreak of the COVID-19 pandemic, aiming to understand the effectiveness of machine learning models in the face of significant disruptive factors like the pandemic. The findings demonstrate that despite the influence of COVID-19, machine learning remains effective. LSTM performs the best among the models, both in traditional mode and after fuzzy interval enhancement, followed by the ELM and RF models. The predictive results of these three models reach a certain level of accuracy and all outperform the BPN model. Fuzzy-LSTM effectively predicts at a 68% confidence level, while Fuzzy-ELM and Fuzzy-RF yield better results at a 95% confidence level. Fuzzy-BPN exhibits the lowest predictive accuracy. Overall, the fuzzy interval-based LSTM excels in time series prediction, suggesting its potential application in forecasting time series data in financial markets to enhance the efficacy of investment analysis for investors.

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A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic

Cited by 35 publications

References 65 publications

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

Combining the dynamic model and deep neural networks to identify the intensity of interventions during COVID-19 pandemic

A Time Series Approach to Smart City Transformation: The Problem of Air Pollution in Brescia

Application of hybrid fuzzy interval-based machine learning models on financial time series — A case study of Taiwan biotech index during the epidemic period

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