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Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review
S Salcedo-Sanz, J Pérez-Aracil, G Ascenso… - Theoretical and Applied …, 2024 - Springer
Atmospheric extreme events cause severe damage to human societies and ecosystems.
The frequency and intensity of extremes and other associated events are continuously …
The frequency and intensity of extremes and other associated events are continuously …
A review of recent and emerging machine learning applications for climate variability and weather phenomena
MJ Molina, TA O'Brien, G Anderson… - … Intelligence for the …, 2023 - journals.ametsoc.org
Climate variability and weather phenomena can cause extremes and pose significant risk to
society and ecosystems, making continued advances in our physical understanding of such …
society and ecosystems, making continued advances in our physical understanding of such …
Highly accurate energy consumption forecasting model based on parallel LSTM neural networks
The main challenges of the energy consumption forecasting problem are the concerns for
reliability, stability, efficiency and accuracy of the forecasting methods. The existing …
reliability, stability, efficiency and accuracy of the forecasting methods. The existing …
A machine learning tutorial for operational meteorology. Part I: Traditional machine learning
RJ Chase, DR Harrison, A Burke… - Weather and …, 2022 - journals.ametsoc.org
Recently, the use of machine learning in meteorology has increased greatly. While many
machine learning methods are not new, university classes on machine learning are largely …
machine learning methods are not new, university classes on machine learning are largely …
A graph-based LSTM model for PM2. 5 forecasting
Accuracy prediction of air quality is of crucial importance for people to take precautions and
improve environmental conditions. By introducing adjacency matrix in Long Short-Term …
improve environmental conditions. By introducing adjacency matrix in Long Short-Term …
A review of machine learning for convective weather
A McGovern, RJ Chase, M Flora… - … Intelligence for the …, 2023 - journals.ametsoc.org
We present an overview of recent work on using artificial intelligence (AI)/machine learning
(ML) techniques for forecasting convective weather and its associated hazards, including …
(ML) techniques for forecasting convective weather and its associated hazards, including …
Prediction compressive strength of concrete containing GGBFS using random forest model
Improvement of compressive strength prediction accuracy for concrete is crucial and is
considered a challenging task to reduce costly experiments and time. Particularly, the …
considered a challenging task to reduce costly experiments and time. Particularly, the …
Warn-on-Forecast System: From vision to reality
PL Heinselman, PC Burke, LJ Wicker… - Weather and …, 2024 - journals.ametsoc.org
In 2009, advancements in NWP and computing power inspired a vision to advance
hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This …
hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This …
[HTML][HTML] The Effect of Energy Consumption, Income, and Population Growth on CO2 Emissions: Evidence from NARDL and Machine Learning Models
With population and income growth, the need for energy has increased in develo** and
emerging economies, which has inevitably led to an increase in carbon dioxide emissions …
emerging economies, which has inevitably led to an increase in carbon dioxide emissions …
Machine learning classification of significant tornadoes and hail in the United States using ERA5 proximity soundings
VA Gensini, C Converse, WS Ashley… - Weather and …, 2021 - journals.ametsoc.org
Previous studies have identified environmental characteristics that skillfully discriminate
between severe and significant-severe weather events, but they have largely been limited …
between severe and significant-severe weather events, but they have largely been limited …