Machine learning algorithms to forecast air quality: a survey
Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is
important to develop forecasting mechanisms that can be used by the authorities, so that …
important to develop forecasting mechanisms that can be used by the authorities, so that …
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 …
Multivariate Time-Series Forecasting: A Review of Deep Learning Methods in Internet of Things Applications to Smart Cities
V Papastefanopoulos, P Linardatos… - Smart Cities, 2023 - mdpi.com
Smart cities are urban areas that utilize digital solutions to enhance the efficiency of
conventional networks and services for sustainable growth, optimized resource …
conventional networks and services for sustainable growth, optimized resource …
A novel intelligent deep learning predictive model for meteorological drought forecasting
A Danandeh Mehr, A Rikhtehgar Ghiasi… - Journal of Ambient …, 2023 - Springer
The advancements of artificial intelligence models have demonstrated notable progress in
the field of hydrological forecasting. However, predictions of extreme climate events are still …
the field of hydrological forecasting. However, predictions of extreme climate events are still …
Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution
G Yu, C Liu, B Tang, R Chen, L Lu, C Cui, Y Hu… - Renewable Energy, 2022 - Elsevier
Accurate regional wind power prediction is of great significance to the wind farm clusters
integration and the economic dispatch of the regional power grid. The complex …
integration and the economic dispatch of the regional power grid. The complex …
A deep learning model for process fault prognosis
Early fault detection and fault prognosis are crucial functions to ensure safe process
operations. Fault prognosis can detect and isolate early developing faults as well as predict …
operations. Fault prognosis can detect and isolate early developing faults as well as predict …
An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2. 5 concentration in urban environment
This study proposes a new model for the spatiotemporal prediction of PM 2.5 concentration
at hourly and daily time intervals. It has been constructed on a combination of three …
at hourly and daily time intervals. It has been constructed on a combination of three …
[HTML][HTML] Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography …
Harnessing the full potential of the metal-based Laser Powder Bed Fusion process (LPBF)
relies heavily on how effectively the overall reliability and stability of the manufactured part …
relies heavily on how effectively the overall reliability and stability of the manufactured part …
A hybrid CNN-GRU model for predicting soil moisture in maize root zone
Soil water content in maize root zone is the main basis of irrigation decision-making.
Therefore, it is important to predict the soil water content at different depths in maize root …
Therefore, it is important to predict the soil water content at different depths in maize root …
A haze prediction model in chengdu based on LSTM
Air pollution with fluidity can influence a large area for a long time and can be harmful to the
ecological environment and human health. Haze, one form of air pollution, has been a …
ecological environment and human health. Haze, one form of air pollution, has been a …