Muhammad Zulqarnain¹, Rozaida Ghazali^1,*, Habib Shah², Lokman Hakim Ismail¹, Abdullah Alsheddy³, Maqsood Mahmud⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3051-3068, 2022, DOI:10.32604/cmc.2022.021629

Abstract Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM_2.5) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM_2.5 concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial manipulating aspects. Many advanced… More >

Xiaorui Shao, Chang Soo Kim^*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5143-5160, 2022, DOI:10.32604/cmc.2022.020689

Abstract

Accurate multi-step PM_2.5 (particulate matter with diameters ≤2.5um) concentration prediction is critical for humankinds’ health and air population management because it could provide strong evidence for decision-making. However, it is very challenging due to its randomness and variability. This paper proposed a novel method based on convolutional neural network (CNN) and long-short-term memory (LSTM) with a space-shared mechanism, named space-shared CNN-LSTM (SCNN-LSTM) for multi-site daily-ahead multi-step PM_2.5 forecasting with self-historical series. The proposed SCNN-LSTM contains multi-channel inputs, each channel corresponding to one-site historical PM_2.5 concentration series. In which, CNN and LSTM are used to extract each… More >

Qiang Liu^1,*, Yanyun Zou^2,3, Xiaodong Liu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 617-637, 2019, DOI:10.32604/cmes.2019.06272

Abstract Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM_2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM_2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM_2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN) containing memory capability… More >