Computational deep air quality prediction techniques: a systematic review
The escalating population and rapid industrialization have led to a significant rise in
environmental pollution, particularly air pollution. This has detrimental effects on both the …
environmental pollution, particularly air pollution. This has detrimental effects on both the …
Effective air quality prediction using reinforced swarm optimization and bi-directional gated recurrent unit
S Gurumoorthy, AK Kokku, P Falkowski-Gilski… - Sustainability, 2023 - mdpi.com
In the present scenario, air quality prediction (AQP) is a complex task due to high variability,
volatility, and dynamic nature in space and time of particulates and pollutants. Recently …
volatility, and dynamic nature in space and time of particulates and pollutants. Recently …
What factors are most closely associated with mood disorders in adolescents during the COVID-19 pandemic? A cross-sectional study based on 1,771 adolescents in …
Background and Aims: COVID-19 has been proven to harm adolescents' mental health, and
several psychological influence factors have been proposed. However, the importance of …
several psychological influence factors have been proposed. However, the importance of …
Prediction of f-CaO content in cement clinker: A novel prediction method based on LightGBM and Bayesian optimization
X Hao, Z Zhang, Q Xu, G Huang, K Wang - Chemometrics and Intelligent …, 2022 - Elsevier
The content of free calcium oxide (f-CaO) in cement clinker is an important index affecting
the quality of cement clinker. Because f-CaO content in cement clinker cannot be measured …
the quality of cement clinker. Because f-CaO content in cement clinker cannot be measured …
PSO-Stacking improved ensemble model for campus building energy consumption forecasting based on priority feature selection
Building energy consumption forecasting plays an indispensable role in energy resource
management and scheduling. When using an ensemble forecasting model, it is difficult to …
management and scheduling. When using an ensemble forecasting model, it is difficult to …
Prediction of dissolved oxygen in aquaculture based on gradient boosting decision tree and long short-term memory network: A study of Chang Zhou fishery …
J Huan, H Li, M Li, B Chen - Computers and Electronics in Agriculture, 2020 - Elsevier
In order to further improve the prediction accuracy of dissolved oxygen (DO) in aquaculture,
a prediction model of DO is proposed by combining the gradient boosting decision tree …
a prediction model of DO is proposed by combining the gradient boosting decision tree …
Real-time rainfall-runoff prediction using light gradient boosting machine coupled with singular spectrum analysis
Z Cui, X Qing, H Chai, S Yang, Y Zhu, F Wang - Journal of Hydrology, 2021 - Elsevier
Urban rainfall-runoff prediction is an effective method for flood mitigation. However, it is
difficult to realize real-time and accurate prediction due to the strong nonlinearity and …
difficult to realize real-time and accurate prediction due to the strong nonlinearity and …
[HTML][HTML] A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations
AE Sikorska-Senoner, JM Quilty - Environmental Modelling & Software, 2021 - Elsevier
A novel ensemble-based conceptual-data-driven approach (CDDA) is developed where a
data-driven model (DDM) is used to “correct” the residuals from an ensemble of hydrological …
data-driven model (DDM) is used to “correct” the residuals from an ensemble of hydrological …
[HTML][HTML] Short-Term Prediction of PM2.5 Using LSTM Deep Learning Methods
This paper implements deep learning methods of recurrent neural networks and short-term
memory models. Two kinds of time-series data were used: air pollutant factors, such as O3 …
memory models. Two kinds of time-series data were used: air pollutant factors, such as O3 …
Ultra-short-term power forecast method for the wind farm based on feature selection and temporal convolution network
W Zha, J Liu, Y Li, Y Liang - ISA transactions, 2022 - Elsevier
The random fluctuation of wind energy is so strong that the output power cannot be
predicted in time and accurately, which will influence the safety and stability of the power …
predicted in time and accurately, which will influence the safety and stability of the power …