Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting
SM Cabaneros, B Hughes - Environmental Modelling & Software, 2022 - Elsevier
The use of data-driven techniques such as artificial neural network (ANN) models for
outdoor air pollution forecasting has been popular in the past two decades. However …
outdoor air pollution forecasting has been popular in the past two decades. However …
PM2. 5 volatility prediction by XGBoost-MLP based on GARCH models
H Dai, G Huang, H Zeng, F Zhou - Journal of cleaner production, 2022 - Elsevier
In recent, air pollution has a sever impact on public health and economy development
throughout the world. Air pollution consists of a variety of harming components, of which fine …
throughout the world. Air pollution consists of a variety of harming components, of which fine …
[HTML][HTML] Are smart cities green? The role of environmental and digital policies for Eco-innovation in China
In this paper, we employ negative binomial and quasi-natural experimental methods (ie,
Difference-in-Differences and Propensity Score Matching), whereby we examine the joint …
Difference-in-Differences and Propensity Score Matching), whereby we examine the joint …
Artificial intelligence in pollution control and management: status and future prospects
Environmental pollution is becoming serious worldwide and remains a big challenge for
human beings in this century. Many countries and organizations are seeking solutions to this …
human beings in this century. Many countries and organizations are seeking solutions to this …
An integrated system to significant wave height prediction: Combining feature engineering, multi-criteria decision making, and hybrid kernel density estimation
K Wang, Y Liu, Q Xing, Y Qian, J Wang, M Lv - Expert Systems with …, 2024 - Elsevier
Accurate prediction of significant wave height is paramount for the effective design,
operation, and maintenance of wave energy converters. However, current research falls …
operation, and maintenance of wave energy converters. However, current research falls …
Updated prediction of air quality based on kalman-attention-LSTM network
H Zhou, T Wang, H Zhao, Z Wang - Sustainability, 2022 - mdpi.com
The WRF-CMAQ (Weather research and forecast-community multiscale air quality)
simulation system is commonly used as the first prediction model of air pollutant …
simulation system is commonly used as the first prediction model of air pollutant …
A combined prediction system for PM2. 5 concentration integrating spatio-temporal correlation extracting, multi-objective optimization weighting and non-parametric …
J Wang, Y Qian, Y Gao, M Lv, Y Zhou - Atmospheric Pollution Research, 2023 - Elsevier
Air pollution nowadays has seriously hindered the sustainable development. PM 2.5 greatly
affects air quality and human health, even facilitates virus transmission, making its …
affects air quality and human health, even facilitates virus transmission, making its …
Digital infrastructure empowerment and urban carbon emissions: Evidence from China
K Liao, J Liu - Telecommunications Policy, 2024 - Elsevier
China is accelerating its entry into the digital age, and the contribution of digital infrastructure
to the reduction of carbon emissions is becoming increasingly prominent. The influence …
to the reduction of carbon emissions is becoming increasingly prominent. The influence …
FedDeep: A Federated Deep Learning Network for Edge Assisted Multi-Urban PM2.5 Forecasting
Y Hu, N Cao, W Guo, M Chen, Y Rong, H Lu - Applied Sciences, 2024 - mdpi.com
Accurate urban PM 2.5 forecasting serves a crucial function in air pollution warning and
human health monitoring. Recently, deep learning techniques have been widely employed …
human health monitoring. Recently, deep learning techniques have been widely employed …
Exploration of multi-scale reconstruction framework in dam deformation prediction
R Yuan, C Su, E Cao, S Hu, H Zhang - Applied Sciences, 2021 - mdpi.com
Affected by various complex factors, dam deformation monitoring data usually reflect
volatility and non-linear characteristics, and traditional prediction models are difficult to …
volatility and non-linear characteristics, and traditional prediction models are difficult to …