An application of sample entropy to precipitation in Paraíba State, Brazil

Xavier, Sílvio Fernando Alves; Jale, Jader da Silva; Stošić, Tatijana; Santos, Carlos Antônio Costa dos; Singh, Vijay P.

doi:10.1007/s00704-018-2496-3

Cited by 34 publications

(16 citation statements)

References 21 publications

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“…In dynamic theory, entropy is an important indicator of the uncertainty measurement of a nonlinear time series [ 13 ]. There are many entropy algorithms that can characterize the randomness of a time series, such as approximate entropy (AE) [ 14 ], sample entropy (SE) [ 15 ], and permutation entropy [ 16 ]. Among them, the permutation entropy (PE) proposed in [ 17 ] is widely used in mechanical fault diagnosis [ 18 ], agricultural commodity analysis [ 16 ], financial sequence analysis [ 19 ], and others, due to its fast operation speed and excellent stability.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Xie

Esmaiel

Sun

et al. 2020

Entropy

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Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

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Section: Introductionmentioning

confidence: 99%

Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Xie

Esmaiel

Sun

et al. 2020

Entropy

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“…The resulting complex dynamics are characterized by properties, which can be revealed by novel methods that have originated in complex system science (Sivakumar and Singh, 2012). Nonlinearity and complexity of rainfall dynamics were studied using chaos theory (Porporato and Ridolfi, 1996), fractal and multifractal analysis (Olsson et al ., 1993; Kantelhardt et al ., 2006; García‐Marín et al ., 2020), information measures (Pierini et al ., 2011; Poveda and Salas, 2015; Xavier et al ., 2019), and complex networks (Boers et al ., 2015; Jha and Sivakumar, 2017). However, standardized indices, which serve to classify the degree of the severity of drought/wet conditions, were much less studied from the point of view of complex systems.…”

Section: Introductionmentioning

confidence: 99%

Fisher Shannon analysis of drought/wetness episodes along a rainfall gradient in Northeast Brazil

Silva

Menezes

Telesca

et al. 2020

Intl Journal of Climatology

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We applied the informational approach of the Fisher-Shannon method to study the time series of Standardized Precipitation Index (SPI) derived from monthly rainfall data at 133 gauging stations in Northeast (NE) Brazil. The climate in the study region ranges from tropical humid in the coastal area Zona de Mata to tropical semiarid inland Sertão, passing through a sub-humid transition region Agreste. The time span is from 1962 to 2012, and SPI was calculated for 1, 3, 6, and 12-month time scales. We calculate information quantifiers Shannon entropy power (SEP) and Fisher information measure (FIM) that quantify signal disorder and structural organization, respectively, and define its position in the Fisher Shannon plane (FS). We find that the SPI-1 series that describes short term dry/wet conditions display a higher degree of organization (higher FIM) and a lower degree of disorder (lower SEP) than SPI's that describe medium and long term conditions (SPI-3, SPI-6, and SPI-12). By analysing the spatial distribution of information quantifiers, we find that short term dry/wet conditions (SPI-1) show the lowest disorder and highest organization degree in the deep inland Sertão region and highest disorder and lowest organization degree in the coastal Zona de Mata region, while long term conditions (SPI-12) show the opposite trend. For medium-term conditions (SPI-3 and SPI-6), the SEP is highest and FIM lowest in the transition sub-humid Agreste region. Our findings indicate that information theory-based methods, particularly FS have the potential to discriminate among different climatic regimes.

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“…Xavier et al . (2019) used the sample entropy to analyse the precipitation complexity in Paraíba, Brazil and explained that the uncertainty in precipitation restricts the development and utilization of local water resources. Luan et al .…”

Section: Introductionmentioning

confidence: 99%

“…However, the reasonable selection of the similar tolerance in the application process has not been effectively solved. In this regard, predecessors have mostly relied on experience (Xavier et al ., 2019), which introduces a certain randomness and will affect the accuracy of precipitation complexity measurement results. The accuracy of precipitation complexity measurement will directly affect the accurate analysis of its driving mechanism, which is related to the human understanding of the uncertainties in climate and hydrological systems under changing environments.…”

Section: Introductionmentioning

confidence: 99%

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

Zhang

Liu

et al. 2020

Quart J Royal Meteoro Soc

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Sample entropy can be used to investigate the complexity of precipitation series. However, the randomness of similar tolerance selection may lead to inaccurate results. To solve this problem, the distinction degree theory was introduced to optimize the similar tolerance of sample entropy, and a specific reference frame for the optimization process was provided. The optimized sample entropy was used to study the spatial differences in precipitation complexity and the possible causes from the perspective of the monthly precipitation (MP) and extreme daily precipitation (EDP) in Heilongjiang Province, China. The results demonstrated that the MP and EDP in Heilongjiang Province both had unsteady complex fluctuation characteristics and the optimal similar tolerances of the sample entropy were 0.24 and 0.13 times the standard deviation, respectively. The complexity of the EDP was higher than that of the MP, and their fluctuation ranges were 1.641–2.268 and 0.433–0.870, respectively. The complexity of the MP in the study area could be spatially represented as a basic pattern, which gradually decreased from east to west. The spatial distribution pattern of the EDP complexity indicated that the northern part was higher than the southern part. Altitude was the common influencing factor of the MP and EDP complexities, and it had a greater impact on the spatial pattern of the EDP complexity. Potential factors influencing the spatial difference in MP complexity also included changes in precipitation intensity, water area and residential and industrial land areas. Except for altitude, industrial economic development level may also affect the complexity of the EDP. The results may improve the application of sample entropy in climate system complexity measurements and could provide guiding significance for revealing the spatial variation of precipitation complexity.

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An application of sample entropy to precipitation in Paraíba State, Brazil

Cited by 34 publications

References 21 publications

Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Fisher Shannon analysis of drought/wetness episodes along a rainfall gradient in Northeast Brazil

Spatial variability and possible cause analysis of regional precipitation complexity based on optimized sample entropy

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