PFVAE: a planar flow-based variational auto-encoder prediction model for time series data
Prediction based on time series has a wide range of applications. Due to the complex
nonlinear and random distribution of time series data, the performance of learning prediction …
nonlinear and random distribution of time series data, the performance of learning prediction …
[HTML][HTML] A systematic survey of air quality prediction based on deep learning
Z Zhang, S Zhang, C Chen, J Yuan - Alexandria Engineering Journal, 2024 - Elsevier
The impact of air pollution on public health is substantial, and accurate long-term predictions
of air quality are crucial for early warning systems to address this issue. Air quality prediction …
of air quality are crucial for early warning systems to address this issue. Air quality prediction …
Deep spatio-temporal graph network with self-optimization for air quality prediction
The environment and development are major issues of general concern. After much
suffering from the harm of environmental pollution, human beings began to pay attention to …
suffering from the harm of environmental pollution, human beings began to pay attention to …
Variational bayesian network with information interpretability filtering for air quality forecasting
Air quality plays a vital role in people's health, and air quality forecasting can assist in
decision making for government planning and sustainable development. In contrast, it is …
decision making for government planning and sustainable development. In contrast, it is …
A comparison of machine learning models for predicting rainfall in urban metropolitan cities
Current research studies offer an investigation of machine learning methods used for
forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their …
forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their …
[HTML][HTML] Forecasting hourly PM2. 5 concentrations based on decomposition-ensemble-reconstruction framework incorporating deep learning algorithms
P Cai, C Zhang, J Chai - Data Science and Management, 2023 - Elsevier
Accurate predictions of hourly PM 2.5 concentrations are crucial for preventing the harmful
effects of air pollution. In this study, a new decomposition-ensemble framework incorporating …
effects of air pollution. In this study, a new decomposition-ensemble framework incorporating …
Using AI and ML to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during COVID-19 pandemic
MB Mariappan, K Devi, Y Venkataraman… - … International Journal of …, 2023 - emerald.com
Purpose This paper aims to address the pressing problem of prediction concerning
shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 …
shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 …
Forecasting of Beijing PM2. 5 with a hybrid ARIMA model based on integrated AIC and improved GS fixed-order methods and seasonal decomposition
L Zhao, Z Li, L Qu - Heliyon, 2022 - cell.com
Abstract Accurate particulate matter 2.5 (PM 2.5) prediction plays a crucial role in the
accurate management of air pollution and prevention of respiratory diseases. However, PM …
accurate management of air pollution and prevention of respiratory diseases. However, PM …
Data-driven ensemble learning approach for optimal design of cantilever soldier pile retaining walls
Cantilever soldier pile retaining walls are used to ensure the stability of excavations. This
paper deploys ensemble machine learning algorithms towards achieving optimum design of …
paper deploys ensemble machine learning algorithms towards achieving optimum design of …
Examining the relationship between land use/land cover (LULC) and land surface temperature (LST) using explainable artificial intelligence (XAI) models: a case …
Understanding the relationship between land use/land cover (LULC) and land surface
temperature (LST) has long been an area of interest in urban and environmental study …
temperature (LST) has long been an area of interest in urban and environmental study …