Particle dispersion for indoor air quality control considering air change approach: A novel accelerated CFD-DNN prediction

Kek, Hong Yee; Bazgir, Adib; Tan, Huiyi; Lee, Chew Tin; Hong, Taehoon; Othman, Mohd Hafiz Dzarfan; Fan, Yee Van; Mat, Mohamad Nur Hidayat; Zhang, Yuwen; Wong, Keng Yinn

doi:10.1016/j.enbuild.2024.113938

Cited by 12 publications

(1 citation statement)

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“…Although machine learning models such as ANNs, CNNs, and LSTM networks can improve the accuracy of forecasting results, they present certain issues in nonlinear modeling and require a long time for model training. Deep neural network (DNN) models [48,49] offer a distinct advantage in nonlinear modeling that facilitates the establishment of nonlinear predictive models capable of accurately reflecting the relationship between fugitive dust concentrations and monitoring factors. As such, they are extremely beneficial in enhancing the accuracy of forecasting outcomes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Dust Emissions in Highway Subgrade-Filling Construction Based on Deep Neural Network

Wang,

Feng,

et al. 2024

HighTech. Innov. J.

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Dust pollution can harm the urban environment and the health of citizens. Each stage in highway construction generates unorganized dust emissions to varying degrees, which complicates their quantification. To precisely forecast dust emissions during the construction of highway subgrades and reduce the associated pollution risks, this study introduces a predictive model based on a deep neural network (DNN) for dust emissions during highway subgrade-filling operations. Dust concentration is treated as a nonlinear multivariate problem, with predictive indicators encompassing particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ground surface temperature, wind speed, air temperature, surface pressure, and relative humidity. Using a DNN model, this study forecasts the concentrations of PM2.5 and PM10 at highway construction sites. Based on a highway project in Hebei Province, this study predicts dust-emission concentrations via field monitoring conducted using self-developed equipment. The model’s predictions exhibit a small mean-absolute-percentage error and root-mean-square error compared with the actual values, and the model’s accuracy significantly surpasses that of conventional regression models. Accurate forecasting can facilitate the timely control of dust concentrations at construction sites, thus facilitating more environmentally friendly and efficient construction. Doi: 10.28991/HIJ-2024-05-02-03 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%