Prediction of Atmospheric Pollutants in Urban Environment Based on Coupled Deep Learning Model and Sensitivity Analysis

Wang, Siyuan; Ren, Ying; Li, Kai; Xia, Bisheng; Li, Huiming

doi:10.2139/ssrn.4356422

Cited by 2 publications

(1 citation statement)

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“…Majorly, air quality-related research and researchers aim emerging nations, although the focus on highly dangerous contaminants such as PM2.5 is established at multiple levels in developing countries. Wang S., et al (2023) executes the quantification, alleviation, and assessment of these impacts need techniques for evaluating the involvement of traffic volumes in air pollution. Conventionally, this is implemented by incorporating an evaluation (both noticed or modelled) of traffic volume, or instead of the allied pollution generation evaluated with weather monitoring in distribution techniques that determine a set of partial differential equations (PDE) for estimating the preferred pollution distributions.…”

Section: Introductionmentioning

confidence: 99%

Air Pollution Monitoring Approach using Atomic Orbital Search Algorithm with Deep Learning Driven

2023

Global NEST: The International Journal

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<p>Air pollution is a major reason for health-related issues and weather changes, one of humanity's most dangerous problems. It is the most crucial environmental issue in the 21st century and has attracted global attention. These challenges are exacerbated by an overabundance of automobiles, industrial output pollution, transportation fuel consumption, and energy generation. Therefore, air pollution prediction was developed vital. Air pollution monitoring is the procedure of analyzing and measuring the air quality in a certain place to develop the levels of several pollutants and contaminants present. Monitoring air pollution is vital to understand its sources, and effects on the environment and human health, and for executing methods for mitigating its effects. Deep learning (DL) approaches are employed for air pollution predicting methods. Therefore, this study develops an Atomic Orbital Search Algorithm with a Deep Learning-Driven Air Pollution Monitoring (AOSADL-APM) approach. The purpose of the AOSADL-APM technique is to predict and classify the presence of air pollutants. In the presented AOSADL-APM technique, the min-max normalization approach is applied for data pre-processing. For air pollution prediction and classification, the AOSADL-APM technique applies deep long short-term memory (DLSTM) methodology. To enhance the performance of the AOSADL-APM technique, the AOSA-based hyperparameter tuning has been developed. The simulation results of the AOSADL-APM technique were tested using the benchmark dataset. The widespread outcome analyzed the greater solution of the AOSADL-APM algorithm compared to existing approaches.</p>

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Section: Introductionmentioning

confidence: 99%