A Hierarchical Pattern Learning Framework for Forecasting Extreme Weather Events

Wang, Dawei; Ding, Wei

doi:10.1109/icdm.2015.93

Cited by 17 publications

(5 citation statements)

References 17 publications

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“…In a recent work [67], regression models based on extreme value theory have been developed to automatically discover sparse temporal dependencies and make predictions in multivariate extreme value time series. Other approaches for predicting extreme weather events such as abnormally high rainfall, floods, and tornadoes using climate data have also been explored in [76], [77], [78]. Effective prediction of geoscience variables can benefit from recent advances in machine learning such as transfer learning [79], where the model trained on a present task (with sufficient number of training samples) is used to improve the prediction performance on a future task with limited number of training samples.…”

Section: Long-term Forecasting Of Geoscience Variablesmentioning

confidence: 99%

Machine Learning for the Geosciences: Challenges and Opportunities

Karpatne

Ebert‐Uphoff

Ravela

et al. 2019

IEEE Trans. Knowl. Data Eng.

421

197

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Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (ML)-that has been widely successful in commercial domains-offers immense potential to contribute to problems in geosciences. However, problems in geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences. We first highlight typical sources of geoscience data and describe their properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the geosciences, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.

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Section: Long-term Forecasting Of Geoscience Variablesmentioning

confidence: 99%

Machine Learning for the Geosciences: Challenges and Opportunities

Karpatne

Ebert‐Uphoff

Ravela

et al. 2019

IEEE Trans. Knowl. Data Eng.

421

197

View full text Add to dashboard Cite

show abstract

“…But different research works define different criteria for instantiating the merging operation. For example, Wang and Ding [178] merge neighborhoods if the unified region after merging can maintain the spatially frequent patterns. Xiong et al [185] chose an alternative approach by merging spatial neighbor locations into the current locations sequentially until the merged region possesses event data that is sufficiently statistically 94:12 L. Zhao significant.…”

Section: Raster-based Location Predictionmentioning

confidence: 99%

Event Prediction in the Big Data Era

Zhao

2021

ACM Comput. Surv.

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This article aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions are discussed. Additional resources related to event prediction are included in the paper website: http://cs.emory.edu/∼lzhao41/projects/event_prediction_site.html.

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“…But different research works define different criteria for instantiating the merging operation. For example, Wang and Ding [211] merge neighborhoods if the unified region after merging can maintain the spatially frequent patterns. Xiong et al [220] chose an alternative approach by merging spatial neighbor locations into the current locations sequentially until the merged region possesses event data that is sufficiently statistically significant.…”

Section: Raster-based Location Predictionmentioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

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Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

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A Hierarchical Pattern Learning Framework for Forecasting Extreme Weather Events

Cited by 17 publications

References 17 publications

Machine Learning for the Geosciences: Challenges and Opportunities

Machine Learning for the Geosciences: Challenges and Opportunities

Event Prediction in the Big Data Era

Event Prediction in Big Data Era: A Systematic Survey

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