Hurst exponent approach through rescaled range analysis to study the time series of summer monsoon rainfall over northeast India

Pal, Srishti; Dutta, Shibsankar; Nasrin, Tamanna; Chattopadhyay, Surajit

doi:10.1007/s00704-020-03338-6

Cited by 27 publications

(16 citation statements)

References 42 publications

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“…Saha and Chattopadhyay (2020) carried out theory-based investigation pertaining the time series of rainfall in seasonal scale as well as yearly scale in the Himalayas during the summer monsoon. Pal et al (2020) carried out study to investigate the behaviour of the time series of rainfall during the summer in northeast India. Bagirov et al (2017) reported that numerous data-driven prediction models such as linear multiple regression (LMR), autoregressive integrated moving average (ARIMA), the K-nearest-neighbours (K-NN), artificial neural network (ANN), and support vector machines for regression (SVMreg) are used for rainfall forecasting.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Khastagir

Hossain

Anwar

2022

Meteorol Atmos Phys

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Precipitation is one of the most intrinsic resources for manifold industrial activities all over Western Australia; consequently, immaculate rainfall prediction is indispensable for flood mitigation as well as water resources management. This study investigated the performance of artificial neural networks (ANN) and Linear multiple regression (LMR) analysis to forecast long-term seasonal spring rainfall in Western Australia, using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential climatic phenomena. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm and subsequently LMR was used with statistical significance for future spring rainfall forecast. The total climatic dataset has been divided into calibration and testing phases to determine the efficacy of the developed models. Different statistical skill tests such as root mean square error (RMSE), mean absolute error (MAE), and Willmott index of agreement ‘d’ were used to assess the efficacy of LMR and ANN modelling. In general, LMR has lower MAE and RMSE values as compared to ANN for most of the stations during calibration and testing periods, whereas ANN models performed better than LMR models based on ‘d’ values. The overall statistical analysis paradigm suggests the efficacy of LMR over ANN models for rainfall forecasting using more climatic variables. As a result, the developed LMR model, incorporated with lagged global climate indices, will facilitate the adequate preparedness for the risks associated with potential droughts in the study region.

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Section: Introductionmentioning

confidence: 99%

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Khastagir

Hossain

Anwar

2022

Meteorol Atmos Phys

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“…At this juncture, it may be noted that the present paper explores the homogenized rainfall data available at the data archival of the Indian Institute of Tropical Meteorology (IITM) (Government of India). The rainfall data presented in the link of the IITM for monthly, seasonal and annual rainfall are in the 10th of millimeter (mm), and the descriptive and inferential part of the current study utilizes the data in the scale similar to that given by the IITM (Pal et al 2020). The data are homogenized by the IITM itself using the procedure developed in Parthasarathy et al (1993).…”

Section: Data and Materialsmentioning

confidence: 99%

Rescaled range analysis and conditional probability-based probe into the intrinsic pattern of rainfall over North Mountainous India

Sharma

Chattopadhyay

2021

Journal of Water and Climate Change

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In work reported here, we have explored rainfall over North Mountainous India for pre-monsoon (MAM), Indian summer monsoon (JJAS), post-monsoon (OND) and Annual. The dependence of JJAS on MAM and OND on JJAS has been explored through conditional probabilities utilizing frequency distribution. An autocorrelation structure has shown that a low lag-1 autocorrelation coefficient characterizes all the time series. We have implemented rescaled range analysis. Through Hurst's exponent and fractal dimension, we have observed that the MAM time series of rainfall over North Mountainous India has a smooth trend and low volatility. We have further observed that for MAM and JJAS, we have , and D is closer to 1 than to 2. However, we have further observed that for OND and Annual rainfall over North Mountainous India and . Therefore, these two time series have been characterized by high volatility and randomness.

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“…Indeed, since decades ago, some studies have used algorithms based on fractals to characterize rainfalls, predict climatology and behavior of water processes [11][12][13][14][15][16][17], some of which have been dealt with the Hurst exponent (H) or the Higuchi fractal dimension (HFD) [16,18]. In addition, such methodologies have been used to quantify the persistency, anti-persistency or randomness of the data from the different weather stations with monthly records, in different periods [10,19].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the most used method to calculate Hurst exponent is rescaled range because of their simplicity to calculate it using statistical tools, [19,22], however, it has some disadvantages against trends, Kantelhardt et al [23]. Rescaled range is not the only method to calculate Hurst exponent, wavelets [24], power spectrum [25] and Annys Lloyd [26] methods have been also reported.…”

Section: Introductionmentioning

confidence: 99%

A Climate-Mathematical Clustering of Rainfall Stations in the Río Bravo-San Juan Basin (Mexico) by Using the Higuchi Fractal Dimension and the Hurst Exponent

et al. 2021

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When conducting an analysis of nature’s time series, such as meteorological ones, an important matter is a long-range dependence to quantify the global behavior of the series and connect it with other physical characteristics of the region of study. In this paper, we applied the Higuchi fractal dimension and the Hurst exponent (rescaled range) to quantify the relative trend underlying the time series of historical data from 17 of the 34 weather stations located in the Río Bravo-San Juan Basin, Mexico; these data were provided by the National Water Commission CONAGUA) in Mexico. In this way, this work aims to perform a comparative study about the level of persistency obtained by using the Higuchi fractal dimension and Hurst exponent for each station of the basin. The comparison is supported by a climate clustering of the stations, according to the Köppen classification. Results showed a better fitting between the climate of each station and its Higuchi fractal dimension obtained than when using the Hurst exponent. In fact, we found that the more the aridity of the zone the more the persistency of rainfall, according to Higuchi’s values. In turn, we found more relation between the Hurst exponent and the accumulated amount of rainfall. These are relations between the climate and the long-term persistency of rainfall in the basin that could help to better understand and complete the climatological models of the study region. Trends between the fractal exponents used and the accumulated annual rainfall were also analyzed.

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Hurst exponent approach through rescaled range analysis to study the time series of summer monsoon rainfall over northeast India

Cited by 27 publications

References 42 publications

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Rescaled range analysis and conditional probability-based probe into the intrinsic pattern of rainfall over North Mountainous India

A Climate-Mathematical Clustering of Rainfall Stations in the Río Bravo-San Juan Basin (Mexico) by Using the Higuchi Fractal Dimension and the Hurst Exponent

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