Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering
R Yan, J Liao, J Yang, W Sun, M Nong, F Li - Expert Systems with …, 2021 - Elsevier
Effective air quality forecasting models are helpful for timely prevention and control of air
pollution. However, the spatiotemporal distribution characteristics of air quality have not …
pollution. However, the spatiotemporal distribution characteristics of air quality have not …
A novel validation framework to enhance deep learning models in time-series forecasting
Time-series analysis and forecasting is generally considered as one of the most challenging
problems in data mining. During the last decade, powerful deep learning methodologies …
problems in data mining. During the last decade, powerful deep learning methodologies …
Novel hybrid extreme learning machine and multi-objective optimization algorithm for air pollution prediction
L Bai, Z Liu, J Wang - Applied Mathematical Modelling, 2022 - Elsevier
A novel system regarding deterministic and interval predictions of pollutant concentration is
constructed in this study, which can not only obtain higher prediction accuracy in …
constructed in this study, which can not only obtain higher prediction accuracy in …
Future Challenges of the Electric Vehicle Market Perceived by Individual Drivers from Eastern Poland
In the past few years, it can be seen that the automotive market has been developing quite
rapidly, especially when it comes to electric cars. This is because the development of …
rapidly, especially when it comes to electric cars. This is because the development of …
Value evaluation system of ecological environment damage compensation caused by air pollution
D Liu - Environmental Technology & Innovation, 2021 - Elsevier
Among the types of ecological environmental pollution, air pollution cannot accurately and
accurately calculate the value loss of environmental damage due to its wide distribution …
accurately calculate the value loss of environmental damage due to its wide distribution …
Soft computing model coupled with statistical models to estimate future of stock market
Almost every organization around the globe is working with uncertainty due to inevitable
changes and growth in every sphere of life. These changes affect directly or indirectly the …
changes and growth in every sphere of life. These changes affect directly or indirectly the …
An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application
Air quality control requires real-time monitoring of pollutant concentration distributions in
large urban areas. Estimation models are used for the soft-calibration of low-cost …
large urban areas. Estimation models are used for the soft-calibration of low-cost …
Prediction of Daily Mean PM10 Concentrations Using Random Forest, CART Ensemble and Bagging Stacked by MARS
A novel framework for stacked regression based on machine learning was developed to
predict the daily average concentrations of particulate matter (PM10), one of Bulgaria's …
predict the daily average concentrations of particulate matter (PM10), one of Bulgaria's …
[HTML][HTML] A multi-strategy-mode waterlogging-prediction framework for urban flood depth
Flooding is one of the most disruptive natural disasters, causing substantial loss of life and
property damage. Coastal cities in Asia face floods almost every year due to monsoon …
property damage. Coastal cities in Asia face floods almost every year due to monsoon …
A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems
SSW Fatima, A Rahimi - Machines, 2024 - mdpi.com
Time-series forecasting is crucial in the efficient operation and decision-making processes of
various industrial systems. Accurately predicting future trends is essential for optimizing …
various industrial systems. Accurately predicting future trends is essential for optimizing …