Application of wavelet-packet transform driven deep learning method in PM2. 5 concentration prediction: A case study of Qingdao, China
Air pollution is one of the most serious environmental problems faced by human beings, and
it is also a hot topic in the development of sustainable cities. Accurate PM 2.5 prediction …
it is also a hot topic in the development of sustainable cities. Accurate PM 2.5 prediction …
Hyperspectral image classification based on superpixel pooling convolutional neural network with transfer learning
F **e, Q Gao, C **, F Zhao - Remote sensing, 2021 - mdpi.com
Deep learning-based hyperspectral image (HSI) classification has attracted more and more
attention because of its excellent classification ability. Generally, the outstanding …
attention because of its excellent classification ability. Generally, the outstanding …
Forecasting PM2. 5 concentration using a single-dense layer BiLSTM method
In recent times, particulate matter (PM2. 5) is one of the most critical air quality contaminants,
and the rise of its concentration will intensify the hazard of cleanrooms. The forecasting of …
and the rise of its concentration will intensify the hazard of cleanrooms. The forecasting of …
Classification of multi-spectral data with fine-tuning variants of representative models
Due to rapid urbanization, agriculture drought, and environmental pollution, significant
efforts have been focused on land use and land cover (LULC) multi-spectral scene …
efforts have been focused on land use and land cover (LULC) multi-spectral scene …
Trend-attribute forecasting of hourly PM2. 5 trends in fifteen cities of Central England applying optimized machine learning feature selection
DA Wood - Journal of Environmental Management, 2024 - Elsevier
Abstract Recorded particulate matter (PM2. 5) hourly trends are compared for fifteen urban
recording sites distributed across central England for the period 2018 to 2022. They include …
recording sites distributed across central England for the period 2018 to 2022. They include …
Using convolutional neural networks to build a lightweight flood height prediction model with grad-cam for the selection of key grid cells in radar echo maps
YC Chen, TY Chang, HY Chow, SL Li, CY Ou - Water, 2022 - mdpi.com
Recent climate change has brought extremely heavy rains and widescale flooding to many
areas around the globe. However, previous flood prediction methods usually require a lot of …
areas around the globe. However, previous flood prediction methods usually require a lot of …
[HTML][HTML] Development of a data-driven three-dimensional PM2. 5 forecast model based on machine learning algorithms
Z Han, T Guan, X Wang, X **n, X Song, Y Wang… - … Technology & Innovation, 2025 - Elsevier
Abstract Fine particle matter (PM 2.5) pollution is a global environmental problem and has
significant impacts on air quality and human health. Accurate prediction is crucial for …
significant impacts on air quality and human health. Accurate prediction is crucial for …
The robust study of deep learning recursive neural network for predicting of turbidity of water
S Wan, ML Yeh, HL Ma, TY Chou - Water, 2022 - mdpi.com
Water treatment is an important process, as it improves water quality and makes it better for
any end use, whether it be drinking, industrial use, irrigation, water recreation, or any other …
any end use, whether it be drinking, industrial use, irrigation, water recreation, or any other …
Research on a Novel Hybrid Decomposition–Ensemble Learning Paradigm Based on VMD and IWOA for PM2.5 Forecasting
H Guo, Y Guo, W Zhang, X He, Z Qu - International Journal of …, 2021 - mdpi.com
The non-stationarity, nonlinearity and complexity of the PM2. 5 series have caused
difficulties in PM2. 5 prediction. To improve prediction accuracy, many forecasting methods …
difficulties in PM2. 5 prediction. To improve prediction accuracy, many forecasting methods …
Prediction of whole-body velocity and direction from local leg joint movements in insect walking via LSTM neural networks
Extracting motion information from videos is important for quantifying data from behavioral
experiments to deepen the understanding of generation mechanisms of animal behavior …
experiments to deepen the understanding of generation mechanisms of animal behavior …