Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events

Ray, Arnob; Chakraborty, Tanujit; Ghosh, Dibakar

doi:10.48550/arxiv.2106.08968

Cited by 3 publications

(3 citation statements)

References 84 publications

(107 reference statements)

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“…search introduced to analyze a pixel-based detection for tropical cyclones (TCs) and atmospheric rivers (AC). Another study was conducted to develop the Optimized Ensemble Deep Learning (OEDL) framework [137] to forecast waves.…”

Section: Climate and Environmental Applicationsmentioning

confidence: 99%

Deep Learning Methods Used in Remote Sensing Images: A Review

Rewehel¹,

Li²,

Hamed³

et al. 2023

j. of environ. & earth. sci.

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Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a wide range of modern applications. However, there are some challenges related to the availability of annotated data, the complex nature of data, and model parameterization, which strongly impact performance. In this article, a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures (vintage Convolutional Neural Network, CNN; Fully Convolutional Networks, FCN; encoder-decoder, recurrent networks; attention models, and generative adversarial models). The characteristics, capabilities, and limitations of current DL models were examined, and potential research directions were discussed.

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Section: Climate and Environmental Applicationsmentioning

confidence: 99%

Deep Learning Methods Used in Remote Sensing Images: A Review

Rewehel¹,

Li²,

Hamed³

et al. 2023

j. of environ. & earth. sci.

View full text Add to dashboard Cite

show abstract

“…First-order and second-order phase transition of a system of non-identical oscillators can be predicted through ESN as well [51]. There are also other approaches of machine learning for identification of chimera and solitary states [52,53], generalized synchronization [54], predicting extreme events [55,56] and forecasting COVID-19 spread [57], but, we consider ESN for our study for its less computational cost.…”

Section: Introductionmentioning

confidence: 99%

Role of assortativity in predicting burst synchronization using echo state network

Roy,

Senapati,

Poria

et al. 2021

Preprint

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“…[51]. Several other data-driven methods enjoy widespread appreciation in the current literatures [52][53][54][55][56][57][58] for forecasting extreme events. Our study involves a special kind of Recurrent neural networks (RNN) [59], viz.…”

Section: Introductionmentioning

confidence: 99%

Extreme events in globally coupled chaotic maps

Chowdhury,

Ray,

Mishra

et al. 2021

Preprint

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Understanding and predicting uncertain things are the central themes of scientific evolution. Human beings revolve around these fears of uncertainties concerning various aspects like a global pandemic, health, finances, to name but a few. Dealing with this unavoidable part of life is far tougher due to the chaotic nature of these unpredictable activities. In the present article, we consider a global network of identical chaotic maps, which splits into two different clusters, despite the interaction between all nodes are uniform. The stability analysis of the spatially homogeneous chaotic solutions provides a critical coupling strength, before which we anticipate such partial synchronization. The distance between these two chaotic synchronized populations often deviates more than eight times of standard deviation from its long-term average. The probability density function of these highly deviated values fits well with the Generalized Extreme Value distribution. Meanwhile, the distribution of recurrence time intervals between extreme events resembles the Weibull distribution. The existing literature helps us to characterize such events as extreme events using the significant height. These extremely high fluctuations are less frequent in terms of their occurrence. We determine numerically a range of coupling strength for these extremely large but recurrent events. On-off intermittency is the responsible mechanism underlying the formation of such extreme events. Besides understanding the generation of such extreme events and their statistical signature, we furnish forecasting these events using the powerful deep learning algorithms of an artificial recurrent neural network. This Long Short-Term Memory (LSTM) can offer handy one-step forecasting of these chaotic intermittent bursts. We also ensure the robustness of this forecasting model with two hundred hidden cells in each LSTM layer.

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Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events

Cited by 3 publications

References 84 publications

Deep Learning Methods Used in Remote Sensing Images: A Review

Deep Learning Methods Used in Remote Sensing Images: A Review

Role of assortativity in predicting burst synchronization using echo state network

Extreme events in globally coupled chaotic maps

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