IRF-LSTM: enhanced regularization function in LSTM to predict the rainfall

Bhimavarapu, Usharani

doi:10.1007/s00521-022-07577-8

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…Employing the Panasonic 18650PF Li-ion Battery Dataset for experiments, the proposed model was benchmarked against other established models. Our results underscored the superior performance of the DWT-DE-LSTM, reinforcing its potential as a robust solution for battery charge prediction in real-world applications [10].…”

Section: Introductionsupporting

confidence: 52%

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

Figueroa,

Ghanimi,

et al. 2024

JMC

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The last decade has seen a significant rise in the adoption and development of Electric Vehicles (EVs), driven by environmental concerns, technological advancements, and governmental support. Batteries, central to EVs, have witnessed groundbreaking innovations in terms of energy density, charging speeds, and longevity. Expanding charging infrastructure and the automotive industry's investment in EV research have made them more mainstream. Effective Battery Management (BM), which includes monitoring essential parameters and thermal management, is critical for the longevity and reliability of EVs. Accurate charge prediction, in particular, aids in trip planning, reduces range anxiety and facilitates cost-effective charging coordinated with dynamic electricity pricing. Traditional models like linear regression and Autor-Rgressive Integrated Moving Average (ARIMA) have been standard for EV battery charge prediction. However, these often struggle with the dynamic nature of EV charging data. Even models like the vanilla Long Short-Term Memory (LSTM), which are adept at recognizing long-term patterns, require meticulous hyperparameter tuning. This work introduces the DWT-DE-LSTM model, which utilizes the Discrete Wavelet Transform (DWT) to dissect battery charging data at different resolutions and a Differential Evolution (DE) strategy for model optimization. Tests using the Panasonic 18650PF Li-ion Battery Dataset revealed the superior efficacy of the DWT-DE-LSTM model, emphasizing its suitability for real-world battery charge prediction.

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Section: Introductionsupporting

confidence: 52%

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

Figueroa,

Ghanimi,

et al. 2024

JMC

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“…In contrast, RNN provides various modern algorithms that perform equally well with sequential or time series data, leveraging past observations. LSTM were developed to address the problem of vanishing gradients encountered in traditional RNN when dealing with long- or short-term dependent variables. Understanding both forward and backward sequences within the time series relevant to fractures is crucial when diagnosing hydraulic fracturing morphology in CBM .…”

Section: Methodsmentioning

confidence: 99%

“…The Bi-LSTM represents an improvement over the traditional LSTM model. As a type of the RNN, the LSTM addresses the problem of gradient vanishing associated with basic RNNs by incorporating a gating mechanism .…”

Section: Methodsmentioning

confidence: 99%

Hybrid Deep Learning Approach for Hydraulic Fracture Morphology Diagnosis in Coalbed Methane Wells

Yang,

Gou,

Min

et al. 2024

Energy Fuels

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Accurately diagnosing hydraulic fracture morphology is crucial for coalbed methane (CBM) development and strategy formulation. However, achieving precise and efficient diagnosis of fracture morphology remains a challenge in complex geological and engineering environments. Therefore, this study proposes a hybrid deep learning method based on multivariate time series classification. In this method, the fracturing curve is compared to the "language" of the CBM reservoir, with fracture morphology representing the semantics of this "language". This method integrates a wavelet transform module and a deep encoder−decoder module. The wavelet transform module aims to reduce signal noise and enhance model robustness, while the deep encoder−decoder module extracts latent features from the fracturing curve. Integrating hydraulic fracture morphology recognition into a hybrid deep learning model composed of diverse deep neural networks tailored for specific predictive tasks maximizes the strengths of each network component. This approach was evaluated using real data from CBM blocks in Northern China, supported by ablation experiments, numerical simulations, and microseismic interpretation, demonstrating its effectiveness and applicability. In contrast to traditional time series deep learning models, this method enables more robust extraction of advanced temporal features and delivers high-precision predictions. This novel approach provides an effective solution for diagnosing hydraulic fracturing morphology and contributes to guiding refracturing.

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“…These tools have become incredibly popular over the last decades, and nowadays are widely used for time series prediction in general, [4][5][6][7][8][9] and also specifically applied in the field of weather forecasting [10][11][12][13][14][15][16][17][18][19][20][21][22]. More and more researchers have adopted machine learning techniques instead of classical physically-based models [23], developing increasingly accurate predictors of intense precipitation [24,25].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Convective Storms Trajectory and Intensity by Neural Networks

Borghi,

Guariso,

Sangiorgio

2024

Forecasting

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Convective storms represent a dangerous atmospheric phenomenon, particularly for the heavy and concentrated precipitation they can trigger. Given their high velocity and variability, their prediction is challenging, though it is crucial to issue reliable alarms. The paper presents a neural network approach to forecast the convective cell trajectory and intensity, using, as an example, a region in northern Italy that is frequently hit by convective storms in spring and summer. The predictor input is constituted by radar-derived information about the center of gravity of the cell, its reflectivity (a proxy for the intensity of the precipitation), and the area affected by the storm. The essential characteristic of the proposed approach is that the neural network directly forecasts the evolution of the convective cell position and of the other features for the following hour at a 5-min temporal resolution without a relevant loss of accuracy in comparison to predictors trained for each specific variable at a particular time step. Besides its accuracy (R2 of the position is about 0.80 one hour in advance), this machine learning approach has clear advantages over the classical numerical weather predictors since it runs at orders of magnitude more rapidly, thus allowing for the implementation of a real-time early-warning system.

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IRF-LSTM: enhanced regularization function in LSTM to predict the rainfall

Cited by 12 publications

References 30 publications

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

Hybrid Deep Learning Approach for Hydraulic Fracture Morphology Diagnosis in Coalbed Methane Wells

Forecasting Convective Storms Trajectory and Intensity by Neural Networks

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