Soil Temperature Prediction Using Convolutional Neural Network Based on Ensemble Empirical Mode Decomposition

Hao, Huibowen; Fu, Yu; Li, Qingliang

doi:10.1109/access.2020.3048028

Cited by 32 publications

(27 citation statements)

References 45 publications

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“…In this paper, to extract useful and effective IMF components, CC analysis is employed for IMF selection [34]. CC represents the correlation between signals, defined in Formula (15).…”

Section: Steps Of Fault Diagnosis For Transformers Vibration Signalmentioning

confidence: 99%

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A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Shang

et al. 2021

Entropy

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Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

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“…In this paper, to extract useful and effective IMF components, CC analysis is employed for IMF selection [34]. CC represents the correlation between signals, defined in Formula (15).…”

Section: Steps Of Fault Diagnosis For Transformers Vibration Signalmentioning

confidence: 99%

“…EEMD is also suitable for non-linear and non-stationary signals. It has been widely applied to feature extraction in recent years [15][16][17]. Zhao et al used EEMD method to extract the characteristics of vibration signal in the case of transformer winding fault [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Shang

et al. 2021

Entropy

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“…Generalized likelihood uncertainty estimation (GLUE) approach was implemented to quantify model uncertainty and concluded that ANFIS-SFO produced the most accurate performance. Hao et al (2021) proposed a model called convolutional neural network based on ensemble empirical mode decomposition (EEMD-CNN) to predict soil temperatures at three depths of 5 to 30 cm [15]. They used Statistical properties of the maximum, mean, minimum and variance air temperature as the meteorological input information.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Study Of Artificial Intelligence Applications For Soil Temperature Prediction

Imanian¹,

Cobo²,

Payeur³

et al. 2022

Preprint

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Soil temperature is a fundamental parameter in water resources and engineering. A cost-effective model which can forecast soil temperature accurately is extensively needed. Recently, many studies have applied artificial intelligence (AI) at both surface and underground levels for soil temperature prediction. However, there is no comprehensive and detailed assessment of the performance of different AI approaches in soil temperature estimation, and primarily limited atmospheric variables are used as input data for AI models. In the present study, great varieties of various land and atmospheric variables are applied to evaluate the performance of a wide range of AI methods on soil temperature prediction. Herein, thirteen approaches, from classic regressions to well-established methods of random forest and gradient boosting to advanced AI techniques like multi-layer perceptron and deep learning are taken into account. The results show that AI is a promising approach in climate parameter forecast and deep learning demonstrates the best performance among other models. It has the highest R-squared ranging from 0.957 to 0.980, the lowest NRMSE ranging from 2.237% to 3.287% and the lowest MAE, ranging from 0.510 to 0.743 in predicting soil temperature. The prediction is repeated for different sizes of data, and prediction outcomes confirm the conclusion mentioned above.

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“…Machine learning has recently received much attention in the soil temperature prediction field (Sattari et al 2020;Li et al 2020;Abyaneh et al 2016). It discovers significant underlying patterns in raw data by constructing a model without any human intervention.…”

Section: Introductionmentioning

confidence: 99%

“…Classification is the task of categorizing an input sample data into one of the predefined classes. In the literature, there are several soil temperature prediction studies that apply different machine learning techniques, such as regression (Alizamir et al 2020a;Alizamir et al 2020b;Sattari et al 2020;Abyaneh et al 2016;Li et al 2020) and time series (Zeynoddin et al 2020;Mehdizadeh et al 2020). For example, (Alizamir et al 2020b) proposed a deep echo state network (Deep ESN) regression model for soil temperature prediction at 10 and 20 cm depths.…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC)

KÜÇÜK

Birant

Taşer

2022

J Agr Sci-Tarim Bili

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Soil Temperature Prediction Using Convolutional Neural Network Based on Ensemble Empirical Mode Decomposition

Cited by 32 publications

References 45 publications

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

A Comprehensive Study Of Artificial Intelligence Applications For Soil Temperature Prediction

A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC)

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