Stacked transfer learning for tropical cyclone intensity prediction

Deo, Ratneel Vikash; Chandra, Rohitash; Sharma, Anuraganand

doi:10.48550/arxiv.1708.06539

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…Transfer learning is not only applied to the problem of imbalanced data, some researchers have also applied transfer learning to the prediction of tropical cyclones. Deo et al (2017) assessed the relationship between different types of cyclones by using transfer learning and traditional neural network methods to achieve more stable intensity predictions for tropical cyclones. Pang et al (2021) combined a deep convolutional generative adversarial network (DCGAN) and the YOLOv3 model to propose a New Detection Framework of Tropical Cyclones (NDFTC) with good stability and accuracy.…”

Section: Transfer Learning-based Methodsmentioning

confidence: 99%

USFP: An unbalanced severe typhoon formation prediction framework based on transfer learning

et al. 2023

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IntroductionSevere typhoons, as extreme weather events, can cause a large number of casualties and property damage in coastal areas. There are mainly three kinds of methods for the prediction of severe typhoon formation, which are the numerical-based methods, the statistical-based methods, and the machine learning-based methods. However, existing methods do not consider the unbalance between the number of ordinary typhoon samples and severe typhoon samples, which makes the accuracies of existing methods in the prediction of severe typhoons much lower than that of ordinary typhoons.MethodsIn this paper, we propose an unbalanced severe typhoon formation prediction (USFP) framework based on transfer learning. We first propose a severe typhoon pre-learning model which is used to learn prior knowledge from a constructed balanced dataset. Then, we propose an unbalanced severe typhoon re-learning model which utilizes the prior knowledge learning from the pre-learning model. Our USFP framework fuses three different variables, which are atmospheric variables, sea surface variables, and ocean hydrographic variables.ResultsExtensive experiments based on datasets of three different regions show that our USFP framework outperforms the numerical model IFS of ECMWF and existing machine learning methods.

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Section: Transfer Learning-based Methodsmentioning

confidence: 99%

USFP: An unbalanced severe typhoon formation prediction framework based on transfer learning

et al. 2023

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“…For the purpose of time series, the subject at hand in this study, the term instance denotes an individual time series contained in a time series dataset. The study in [46] discusses two types of Instance-based transfer learning: Instance Selection and Symbolic Aggregation Approximation. In the Instance Selection method, a useful subset of instances from the source domain that are most relevant to the target domain is selected to train the target model [54].…”

Section: Instance-based Transfer Learningmentioning

confidence: 99%

“…All these techniques are being used for transfer learning with time series. The reader may be interested in looking at [44][45][46]. By the way, the use case chosen for implementation in this study is an ensemble technique (see Section 5).…”

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confidence: 99%

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Kambale,

Salem,

Benarbia

et al. 2024

Symmetry

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Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learning is relatively new. This paper addresses the need for empirical studies as identified in recent reviews advocating the need for practical guidelines for Transfer Learning approaches and method designs for time series forecasting. The main contribution of this paper is the suggestion of a comprehensive framework for Transfer Learning Sensitivity Analysis (SA) for time series forecasting. We achieve this by identifying various parameters seen from various angles of transfer learning applied to time series, aiming to uncover factors and insights that influence the performance of transfer learning in time series forecasting. Undoubtedly, symmetry appears to be a core aspect in the consideration of these factors and insights. A further contribution is the introduction of four TL performance metrics encompassed in our framework. These TL performance metrics provide insight into the extent of the transferability between the source and the target domains. Analyzing whether the benefits of transferred knowledge are equally or unequally accessible and applicable across different domains or tasks speaks to the requirement of symmetry or asymmetry in transfer learning. Moreover, these TL performance metrics inform on the possibility of the occurrence of negative transfers and also provide insight into the possible vulnerability of the network to catastrophic forgetting. Finally, we discuss a sensitivity analysis of an Ensemble TL technique use case (with Multilayer Perceptron models) as a proof of concept to validate the suggested framework. While the results from the experiments offer empirical insights into various parameters that impact the transfer learning gain, they also raise the question of network dimensioning requirements when designing, specifically, a neural network for transfer learning.

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“…In addition, transfer learning can be used to complement the target dataset by incorporating knowledge from the dataset. Thus, the authors of [93] developed an effective strategy to evaluate the relationship between different types of cyclones through transfer learning and traditional learning methods, and to then predict the intensity more consistently.…”

Section: Intensity Predictionmentioning

confidence: 99%

Machine Learning in Tropical Cyclone Forecast Modeling: A Review

2020

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Tropical cyclones have always been a concern of meteorologists, and there are many studies regarding the axisymmetric structures, dynamic mechanisms, and forecasting techniques from the past 100 years. This research demonstrates the ongoing progress as well as the many remaining problems. Machine learning, as a means of artificial intelligence, has been certified by many researchers as being able to provide a new way to solve the bottlenecks of tropical cyclone forecasts, whether using a pure data-driven model or improving numerical models by incorporating machine learning. Through summarizing and analyzing the challenges of tropical cyclone forecasts in recent years and successful cases of machine learning methods in these aspects, this review introduces progress based on machine learning in genesis forecasts, track forecasts, intensity forecasts, extreme weather forecasts associated with tropical cyclones (such as strong winds and rainstorms, and their disastrous impacts), and storm surge forecasts, as well as in improving numerical forecast models. All of these can be regarded as both an opportunity and a challenge. The opportunity is that at present, the potential of machine learning has not been completely exploited, and a large amount of multi-source data have also not been fully utilized to improve the accuracy of tropical cyclone forecasting. The challenge is that the predictable period and stability of tropical cyclone prediction can be difficult to guarantee, because tropical cyclones are different from normal weather phenomena and oceanographic processes and they have complex dynamic mechanisms and are easily influenced by many factors.

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Stacked transfer learning for tropical cyclone intensity prediction

Cited by 6 publications

References 21 publications

USFP: An unbalanced severe typhoon formation prediction framework based on transfer learning

USFP: An unbalanced severe typhoon formation prediction framework based on transfer learning

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Machine Learning in Tropical Cyclone Forecast Modeling: A Review

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