Machine learning in tropical cyclone forecast modeling: A review
Tropical cyclones have always been a concern of meteorologists, and there are many
studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting …
studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting …
Transfer learning with time series data: a systematic mapping study
Transfer Learning is a well-studied concept in machine learning, that relaxes the assumption
that training and testing data need to be drawn from the same distribution. Recent success in …
that training and testing data need to be drawn from the same distribution. Recent success in …
Improvement of typhoon intensity forecasting by using a novel spatio-temporal deep learning model
Typhoons can cause massive casualties and economic damage, and accurately predicting
typhoon intensity has always been a hot topic both in theory and practice. In consideration …
typhoon intensity has always been a hot topic both in theory and practice. In consideration …
An adaptive learning approach for tropical cyclone intensity correction
Tropical cyclones (TCs) are dangerous weather events; accurate monitoring and forecasting
can provide significant early warning to reduce loss of life and property. However, the study …
can provide significant early warning to reduce loss of life and property. However, the study …
Tropical cyclone intensity prediction based on recurrent neural networks
The accurate prediction for the tropical cyclone (TC) intensity is a recognised challenge.
Researchers usually develop dynamical models to address this task. However, since the TC …
Researchers usually develop dynamical models to address this task. However, since the TC …
[HTML][HTML] Cyclone trajectory and intensity prediction with uncertainty quantification using variational recurrent neural networks
Cyclone track forecasting is a critical climate science problem involving time-series
prediction of cyclone location and intensity. Machine learning methods have shown much …
prediction of cyclone location and intensity. Machine learning methods have shown much …
Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery
W Tian, P Song, Y Chen, Y Zhang, L Wu… - … in Atmospheric Sciences, 2025 - Springer
Tropical cyclones (TCs) are one of the most serious types of natural disasters, and accurate
TC activity predictions are key to disaster prevention and mitigation. Recently, TC track …
TC activity predictions are key to disaster prevention and mitigation. Recently, TC track …
Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case …
Recently, transfer learning has gained popularity in the machine learning community.
Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge …
Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge …
Community paramedicine supporting community needs: a scoping review
TM Lunn, JL Bolster, AM Batt - Health & Social Care in the …, 2024 - Wiley Online Library
Health and social needs exist along a dynamic continuum. Recognising that health status is
inextricably impacted by social determinants of health, community paramedicine has …
inextricably impacted by social determinants of health, community paramedicine has …
Neuro-evolutionary transfer learning through structural adaptation
Transfer learning involves taking an artificial neural network (ANN) trained on one dataset
(the source) and adapting it to a new, second dataset (the target). While transfer learning …
(the source) and adapting it to a new, second dataset (the target). While transfer learning …