Forecasting Atmospheric Visibility Using Auto Regressive Recurrent Neural Network

Jonnalagadda, Jahnavi; Hashemi, Mahdi

doi:10.1109/iri49571.2020.00037

Cited by 19 publications

(5 citation statements)

References 22 publications

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“…The ENSO impact generates temperature variations, making them slightly warmer or colder up to extreme temperatures, inducing natural disasters. As claimed by Jonnalagadda and Hashemi [43], the use of the adaptive graph convolutional recurrent neural network (AGCRNN) can capture the temporal relationships of features with the Oceanic Niño Index (ONI), increasing the prediction time from three months to eighteen months, surpassing the current dynamic and statistical models.…”

Section: Related Workmentioning

confidence: 99%

An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model

Vázquez-Ramírez,

Torres-Ruiz,

Quintero

et al. 2023

Mathematics

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Several Sun models suggest a radioactive balance, where the concentration of greenhouse gases and the albedo effect are related to the Earth’s surface temperature. There is a considerable increment in greenhouse gases due to anthropogenic activities. Climate change correlates with this alteration in the atmosphere and an increase in surface temperature. Efficient forecasting of climate change and its impacts could be helpful to respond to the threat of c.c. and develop sustainably. Many studies have predicted temperature changes in the coming years. The global community has to create a model that can realize good predictions to ensure the best way to deal with this warming. Thus, we propose a finite-time thermodynamic (FTT) approach in the current work. FTT can solve problems such as the faint young Sun paradox. In addition, we use different machine learning models to evaluate our method and compare the experimental prediction and results.

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Section: Related Workmentioning

confidence: 99%

An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model

Vázquez-Ramírez,

Torres-Ruiz,

Quintero

et al. 2023

Mathematics

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“…Both theoretical [5,6] and practical [7][8][9][10][11] application of ML in forecasting and predicting spatial-temporal phenomena has been underscored in the literature. Random forest and decision tree performed better in rainfall forecasting for shorter lead times [12,13].…”

Section: Literature Reviewmentioning

confidence: 99%

Quality-Aware Conditional Generative Adversarial Networks for Precipitation Nowcasting

Jonnalagadda

Hashemi

2023

Itise 2023

Self Cite

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“…One example of handling temporal developments in fog forecasting involves using different types of recurrent neural networks (RNN) (Jonnalagadda & Hashemi, 2020; Miao et al ., 2020; Pan et al ., 2019; Park et al ., 2022). These networks have already incorporated the concept of considering temporal development into their algorithm's architecture.…”

Section: Introductionmentioning

confidence: 99%

Improving classification‐based nowcasting of radiation fog with machine learning based on filtered and preprocessed temporal data

Schütz,

Bendix

et al. 2023

Quart J Royal Meteoro Soc

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Radiation fog nowcasting remains a complex yet critical task due to its substantial impact on traffic safety and economic activity. Current numerical weather prediction models are hindered by computational intensity and knowledge gaps regarding fog‐influencing processes. Machine‐Learning (ML) models, particularly those employing the eXtreme Gradient Boosting (XGB) algorithm, may offer a robust alternative, given their ability to learn directly from data, swiftly generate nowcasts, and manage non‐linear interrelationships among fog variables. However, unlike recurrent neural networks XGB does not inherently process temporal data, which is crucial in fog formation and dissipation. This study proposes incorporating preprocessed temporal data into the model training and applying a weighted moving‐average filter to regulate the substantial fluctuations typical in fog development. Using an ML training and evaluation scheme for time series data, we conducted an extensive bootstrapped comparison of the influence of different smoothing intensities and trend information timespans on the model performance on three levels: overall performance, fog formation and fog dissipation. The performance is checked against one benchmark and two baseline models. A significant performance improvement was noted for the station in Linden‐Leihgestern (Germany), where the initial F1 score of 0.75 (prior to smoothing and trend information incorporation) was improved to 0.82 after applying the smoothing technique and further increased to 0.88 when trend information was incorporated. The forecasting periods ranged from 60 to 240 min into the future. This study offers novel insights into the interplay of data smoothing, temporal preprocessing, and ML in advancing radiation fog nowcasting.

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Forecasting Atmospheric Visibility Using Auto Regressive Recurrent Neural Network

Cited by 19 publications

References 22 publications

An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model

An Analysis of Climate Change Based on Machine Learning and an Endoreversible Model

Quality-Aware Conditional Generative Adversarial Networks for Precipitation Nowcasting

Improving classification‐based nowcasting of radiation fog with machine learning based on filtered and preprocessed temporal data

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