Prediction of PM10 Concentration in Malaysia Using K-Means Clustering and LSTM Hybrid Model
Following the rapid development of various industrial sectors, air pollution frequently occurs
in every corner of the world. As a dominant pollutant in Malaysia, particulate matter PM10 …
in every corner of the world. As a dominant pollutant in Malaysia, particulate matter PM10 …
Boosted Regression Tree (BRT) model for PM10 concentrations prediction in Malaysia
R Norazrin, HA Hamid, AS Yahaya - IOP Conference Series …, 2023 - iopscience.iop.org
Air pollution in urban areas is a highly complex problem, displaying strong seasonality and
dependence on meteorological factors. Urban particulate matter with an aerodynamic …
dependence on meteorological factors. Urban particulate matter with an aerodynamic …
[HTML][HTML] Forecasting PM2. 5 in Malaysia using a hybrid model
Predicting future PM2. 5 concentrations based on knowledge obtained from past
observational data is very useful for predicting air pollution. This paper aims to develop a …
observational data is very useful for predicting air pollution. This paper aims to develop a …
Statistical modeling approaches for PM10 forecasting at industrial areas of Malaysia
Major industrial areas in Malaysia experience number of unhealthy days because of
extreme impermanent PM10 incidents which are detrimental to human and the environment …
extreme impermanent PM10 incidents which are detrimental to human and the environment …
[PDF][PDF] Comparing the performance of statistical models for predicting PM10 concentrations
AS Sayegh, S Munir, TM Habeebullah - Aerosol and Air Quality …, 2014 - aaqr.org
ABSTRACTThe ability to accurately model and predict the ambient concentration of
Particulate Matter (PM) is essential for effective air quality management and policies …
Particulate Matter (PM) is essential for effective air quality management and policies …
Classification Prediction of PM10 Concentration Using a Tree-Based Machine Learning Approach
The PM10 prediction has received considerable attention due to its harmful effects on
human health. Machine learning approaches have the potential to predict and classify future …
human health. Machine learning approaches have the potential to predict and classify future …
A review of PM10 concentrations modelling in Malaysia
The purpose of predictive modelling is to predict the variable of interest with reasonable
precision, and often to assess the contribution of the independent variables to the …
precision, and often to assess the contribution of the independent variables to the …
[PDF][PDF] Modelling particulate matter (PM10) concentration in industrialized area: A comparative study of linear and nonlinear algorithms
S Abdullah, M Ismail, NNA Samat… - ARPN J. Eng. Appl …, 2018 - researchgate.net
Particulate matter is a critical air pollutant in Malaysia as it is the utmost dominant pollutant,
especially in industrial and urban areas. The development of a robust model for PM10 …
especially in industrial and urban areas. The development of a robust model for PM10 …
A model for particulate matter (PM2. 5) prediction for Delhi based on machine learning approaches
Abstract Particulate matter (PM 2.5) remains one of the most dominant contributors to air
pollution in Delhi and its acute or chronic exposures have exerted serious health …
pollution in Delhi and its acute or chronic exposures have exerted serious health …
[HTML][HTML] Machine learning methods to predict particulate matter PM 2.5
N Palanichamy, SC Haw, S Subramanian… - …, 2022 - ncbi.nlm.nih.gov
Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated
on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data …
on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data …