Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand (original) (raw)

Development of Multiple Linear Regression for Particulate Matter (PM10) Forecasting during Episodic Transboundary Haze Event in Malaysia

Drs najah Ahmed

Atmosphere, 2020

View PDFchevron_right

Prediction of PM2.5 in an Urban Area of Northern Thailand Using Multivariate Linear Regression Model

Teerachai Amnuaylojaroen

Advances in Meteorology

View PDFchevron_right

MULTIPLE LINEAR REGRESSION (MLR) MODELS FOR LONG TERM PM 10 CONCENTRATION FORECASTING DURING DIFFERENT MONSOON SEASONS

Siyuen Fong

View PDFchevron_right

Three-Hour-Ahead of Multiple Linear Regression (MLR) Models for Particulate Matter (PM10) Forecasting

amalina abu mansor

2021

View PDFchevron_right

The Effect of Seasonal Variation and Meteorological Data on PM10 Concentrations in Northern Thailand

Pantitcha Outapa

International Journal of GEOMATE

View PDFchevron_right

A Suitable Model for Spatiotemporal Particulate Matter Concentration Prediction in Rural and Urban Landscapes, Thailand

Pirada Tongprasert

Atmosphere

View PDFchevron_right

Regression trees modeling and forecasting of PM10 air pollution in urban areas

Snezhana Gocheva-Ilieva

Nucleation and Atmospheric Aerosols, 2017

View PDFchevron_right

Estimation of particulate matter from visibility in Bangkok, Thailand

Nitaya Vajanapoom

Journal of Exposure Analysis and Environmental Epidemiology, 2001

View PDFchevron_right

Spatio-temporal modelling of the influence of climatic variables and seasonal variation on PM10 in Malaysia using multivariate regression (MVR) and GIS

Abdul-Lateef Balogun

Geomatics, Natural Hazards and Risk

View PDFchevron_right

Forecasting Air Pollution Particulate Matter (PM 2.5 ) Using Machine Learning Regression Models

Dr. Yogesh K M, Harishkumar kushtagi shetra

Elsevier, 2020

View PDFchevron_right

Prediction and analysis of particulate matter (PM2.5 and PM10) concentrations using machine learning techniques

Dr. Anurag Barthwal

Journal of Ambient Intelligence and Humanized Computing, 2021

View PDFchevron_right

Evaluating the Performance of Random Forest and Multiple Linear Regression for Higher Observed PM10 Concentrations

AIDA WATI ZAINAN ABIDIN

Israa University Journal for Applied Science

View PDFchevron_right

Developing a methodology to predict PM10 concentrations in urban areas using Generalized Linear Models

João Garcia

Environmental technology, 2016

View PDFchevron_right

Comparing the Performance of Statistical Models for Predicting PM10 Concentrations

Turki Habeebullah

Aerosol and Air Quality Research, 2014

View PDFchevron_right

A new dynamic approach using data-driven and machine learning models for forecasting particulate matter in Dhaka megacity

Mustafizur Rahman

Environmental Pollution and Management , 2024

View PDFchevron_right

LINKING AIR QUALITY TO METEOROLOGY: A MULTILINEAR REGRESSION APPROACH

Mohammad Maksimul Islam

Proceedings of the 5th International Conference on Civil Engineering for Sustainable Development (ICCESD 2020), 7~9 February 2020, KUET, Khulna, Bangladesh, 2020

View PDFchevron_right

Comparison of regression models of PM10 particulate concentration in relation to selected meteorological elements based on the example of Sosnowiec, Poland

Agnieszka Ziernicka-Wojtaszek

Időjárás, 2019

View PDFchevron_right

Evaluation of a multiple regression model for the forecasting of the concentrations of NO x and PM 10 in Athens and Helsinki

Spyros Karakitsios

Science of The Total Environment, 2011

View PDFchevron_right

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Azman Azid

Malaysian Journal of Fundamental and Applied Sciences

View PDFchevron_right

Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies

Hamid Taheri Shahraiyni

Atmosphere, 2016

View PDFchevron_right

Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models

Feng Liu

View PDFchevron_right

Pm10 Prediction And Forecasting Using Cart: A Case Study For Pleven, Bulgaria

Snezhana Gocheva-Ilieva

2018

View PDFchevron_right

Robust Regression Models for Predicting PM10 Concentration in an Industrial Area

Hazrul Abdul Hamid

2012

View PDFchevron_right

Exploring key air pollutants and forecasting particulate matter PM10 by a two-step SARIMA approach

Snezhana Gocheva-Ilieva

AIP Conference Proceedings

View PDFchevron_right

Short Term Prediction of PM10Concentrations Using Seasonal Time Series Analysis

Hazrul Abdul Hamid

MATEC Web of Conferences, 2016

View PDFchevron_right

Comparison Between Multiple Linear Regression And Feed forward Back propagation Neural Network Models For Predicting PM10 Concentration Level Based On Gaseous And Meteorological Parameters

Hazrul Abdul Hamid

2011

View PDFchevron_right

AN EXPLORATORY ANALYSIS OF PM10 PARTICULATE MATTER RELATIONSHIPS WITH WEATHER DATA AND SPATIAL VARIATION

Daniel Dunea

14th SGEM GeoConference on ENERGY AND CLEAN TECHNOLOGIES, 2014

View PDFchevron_right

Estimating ground-level PM2.5 using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh

bonosri ghose

Air Quality, Atmosphere & Health

View PDFchevron_right

Developing a methodology to predict PM10urban concentrations using GLM

Filomena Teodoro

WIT Transactions on Ecology and the Environment, 2014

View PDFchevron_right