A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Machine learning applications on air temperature prediction in the urban canopy layer: A critical review of 2011–2022
Air temperature within the urban canopy layer is one of the most critical variables that impact
the environmental sustainability of cities. With advantages in computational speed, machine …
the environmental sustainability of cities. With advantages in computational speed, machine …
All-sky 1 km MODIS land surface temperature reconstruction considering cloud effects based on machine learning
A high spatio-temporal resolution land surface temperature (LST) is necessary for various
research fields because LST plays a crucial role in the energy exchange between the …
research fields because LST plays a crucial role in the energy exchange between the …
Ensemble forecast for storm tide and resurgence from Tropical Cyclone Isaias
Ensemble-based probabilistic forecasting of storm surge is increasingly being used to
provide metrics for emergency management decisions such as the near-worst case …
provide metrics for emergency management decisions such as the near-worst case …
Developing a novel hybrid model based on deep neural networks and discrete wavelet transform algorithm for prediction of daily air temperature
The precise predicting of air temperature has a significant influence in many sectors such as
agriculture, industry, modeling environmental processes. In this work, to predict the mean …
agriculture, industry, modeling environmental processes. In this work, to predict the mean …
Deep learning with autoencoders and LSTM for ENSO forecasting
CC Ibebuchi, MB Richman - Climate Dynamics, 2024 - Springer
Abstract El Niño Southern Oscillation (ENSO) is the prominent recurrent climatic pattern in
the tropical Pacific Ocean with global impacts on regional climates. This study utilizes deep …
the tropical Pacific Ocean with global impacts on regional climates. This study utilizes deep …
Analogue Forecast System for Daily Precipitation Prediction Using Autoencoder Feature Extraction: Application in Hong Kong
YC Tsoi, YT Kwok, MC Lam, WK Wong - arXiv preprint arXiv:2501.02814, 2025 - arxiv.org
In the Hong Kong Observatory, the Analogue Forecast System (AFS) for precipitation has
been providing useful reference in predicting possible daily rainfall scenarios for the next 9 …
been providing useful reference in predicting possible daily rainfall scenarios for the next 9 …
A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation
Reliable early forecasting of extreme summer air temperatures is essential for effectively
managing and mitigating the socioeconomic damage caused by thermal disasters …
managing and mitigating the socioeconomic damage caused by thermal disasters …
An efficient hybrid weather prediction model based on deep learning
Weather events directly affect human activities. In particular, extreme weather events with
global warming, forest fires, and high air temperatures that cause drought make human life …
global warming, forest fires, and high air temperatures that cause drought make human life …
The Importance of Architecture Choice in Deep Learning for Climate Applications
S Dräger, M Sonnewald - arXiv preprint arXiv:2402.13979, 2024 - arxiv.org
Machine Learning has become a pervasive tool in climate science applications. However,
current models fail to address nonstationarity induced by anthropogenic alterations in …
current models fail to address nonstationarity induced by anthropogenic alterations in …