Related references
Note: Only part of the references are listed.A systematic literature review of deep learning neural network for time series air quality forecasting
Nur'atiah Zaini et al.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)
GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms
Abdulwaheed Tella et al.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)
The impact of meteorological conditions on Air Quality Index under different urbanization gradients: a case from Taipei
Zhipeng Zhu et al.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY (2021)
Validation of linear, nonlinear, and hybrid models for predicting particulate matter concentration in Tehran, Iran
Jamil Amanollahi et al.
THEORETICAL AND APPLIED CLIMATOLOGY (2020)
Forecasting PM10 concentrations using time series models: a case of the most polluted cities in Turkey
Hatice Oncel Cekim
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2020)
Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables
Laura Goulier et al.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2020)
Development of Multiple Linear Regression for Particulate Matter (PM10) Forecasting during Episodic Transboundary Haze Event in Malaysia
Samsuri Abdullah et al.
ATMOSPHERE (2020)
a Impact of Air Pollution Regulation and Technological Investment on Sustainable Development of Green Economy in Eastern China: Empirical Analysis with Panel Data Approach
Mingliang Zhao et al.
SUSTAINABILITY (2020)
Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions
Qingehun Guo et al.
AEROSOL AND AIR QUALITY RESEARCH (2020)
Air Quality Prediction Using Improved PSO-BP Neural Network
Yuan Huang et al.
IEEE ACCESS (2020)
A comparison of random forest variable selection methods for classification prediction modeling
Jaime Lynn Speiser et al.
EXPERT SYSTEMS WITH APPLICATIONS (2019)
Evaluation of random forest and regression tree methods for estimation of mass first flush ratio in urban catchments
Minhyuk Jeung et al.
JOURNAL OF HYDROLOGY (2019)
Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China
Wenxuan Xu et al.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2019)
Stability-based Dynamic Bayesian Network method for dynamic data mining
Mohamed Naili et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2019)
Study on Spatial Temporal Distribution Characteristics of Air Quality Index in Beijing and Its Correlation with Local Meteorological Conditions
Jiaqi Guo et al.
DISCRETE DYNAMICS IN NATURE AND SOCIETY (2019)
Air Quality Predictive Modeling Based on an Improved Decision Tree in a Weather-Smart Grid
Yuanni Wang et al.
IEEE ACCESS (2019)
Assessing the Economic and Environmental Sustainability of a Regional Air Quality Plan
Claudio Carnevale et al.
SUSTAINABILITY (2018)
Correlation and variable importance in random forests
Baptiste Gregorutti et al.
STATISTICS AND COMPUTING (2017)
Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models
Hui Wang et al.
ENERGIES (2017)
Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model
Huan Zhang et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2017)
A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
Wei Chen et al.
CATENA (2017)
A hybrid land use regression/AERMOD model for predicting intra-urban variation in PM2.5
Drew R. Michanowicz et al.
ATMOSPHERIC ENVIRONMENT (2016)
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
CJ Willmott et al.
CLIMATE RESEARCH (2005)