scholarly journals A wavelet transform method to determine monsoon onset and retreat from precipitation time‐series

2021 ◽  
Author(s):  
Jorge L. García‐Franco ◽  
Osprey Scott ◽  
Lesley J. Gray
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Monsoon Onset ◽  
Precipitation Time Series ◽  
Transform Method

scholarly journals Advantages Of A Time Series Analysis Using Wavelet Transform As Compared With A Fourier Analysis

2015 ◽  
Vol 23 (2) ◽  
pp. 30-36 ◽  
Author(s):  
Patrik Sleziak ◽  
Kamila Hlavčová ◽  
Ján Szolgay
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Time Series Analysis ◽  
Fourier Analysis ◽  
Stationary Time Series ◽  
Precipitation Time Series ◽  
Software Environment ◽  
R Software ◽  
Series Analysis ◽  
Average Annual Precipitation

Abstract The paper presents an analysis of changes in the structure of the average annual discharges, average annual air temperature, and average annual precipitation time series in Slovakia. Three time series with lengths of observation from 1961 to 2006 were analyzed. An introduction to spectral analysis with Fourier analysis (FA) is given. This method is used to determine significant periods of a time series. Later in this article a description of a wavelet transform (WT) is reviewed. This method is able to work with non-stationary time series and detect when significant periods are presented. Subsequently, models for the detection of potential changes in the structure of the time series analyzed were created with the aim of capturing changes in the cyclical components and the multiannual variability of the time series selected for Slovakia. Finally, some of the comparisons of the time series analyzed are discussed. The aim of the paper is to show the advantages of time series analysis using WT compared with FT. The results were processed in the R software environment.

scholarly journals A multiscale time-space approach to analyze and categorize the precipitation fluctuation based on the wavelet transform and information theory concept

2018 ◽  
Vol 49 (3) ◽  
pp. 724-743 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Vahid Nourani ◽  
Farhad Alizadeh
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Multiple Scales ◽  
Multiscale Entropy ◽  
Discrete Wavelet ◽  
Monthly Precipitation ◽  
Precipitation Time Series ◽  
Rain Gauges ◽  
Time Space ◽  
Precipitation Variation

AbstractThe present study proposed a time-space framework using discrete wavelet transform-based multiscale entropy (DWE) approach to analyze and spatially categorize the precipitation variation in Iran. To this end, historical monthly precipitation time series during 1960–2010 from 31 rain gauges were used in this study. First, wavelet-based de-noising approach was applied to diminish the effect of noise in precipitation time series which may affect the entropy values. Next, Daubechies (db) mother wavelets (db5–db10) were used to decompose the precipitation time series. Subsequently, entropy concept was applied to the sub-series to measure the uncertainty and disorderliness at multiple scales. According to the pattern of entropy across scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation in each cluster. Spatial categorization of rain gauges was performed using DWE values as input data to k-means and self-organizing map (SOM) clustering techniques. According to evaluation criteria, it was proved that k-means with clustering number equal to 5 with Silhouette coefficient=0.33, Davis–Bouldin=1.18 and Dunn index=1.52 performed better in determining homogenous areas. Finally, investigating spatial structure of precipitation variation revealed that the DWE had a decreasing and increasing relationship with longitude and latitude, respectively, in Iran.

The Analysis of the Characteristics of Annual Runoff Series through Wavelet Coefficients

2010 ◽  
Vol 113-116 ◽  
pp. 575-578
Author(s):  
Su Yi Liu
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Yangtze River ◽  
Yellow River ◽  
Annual Runoff ◽  
Wavelet Function ◽  
Wavelet Coefficients ◽  
Precipitation Time Series ◽  
Monthly Data ◽  
Runoff Series

A runoff-sequence contains many frequencies, from the point of the wavelet transfor- mation, they are included in different wavelet coefficients. Therefore, when a smooth wavelet function is used, changes in multi-time scale and jump characteristics of precipitation time series have been probed the tendency. Wavelet transform can clearly demonstrate all kinds of characteristics of precipitation runoff series: the strength and distribution of time scales. The monthly data used in this paper comes from Guidei hydrology station of Yellow River (1919-1994) and Yichang hydrology station of Yangtze River (1946-1976), and the yearly ones from Yichang hydrology station (1882-1984).

scholarly journals ANALYSIS OF THE PRECIPITATION CLIMATE SIGNAL USING EMPIRICAL MODE DECOMPOSITION (EMD) OVER THE CASPIAN CATCHMENT AREA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 923-929
Author(s):  
F. Sabzehee ◽  
V. Nafisi ◽  
S. Iran Pour ◽  
B. D. Vishwakarma
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Precipitation Time Series ◽  
Climate Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Instantaneous Frequencies ◽  
Better Than

Abstract. In this paper, we employ Empirical Mode Decomposition (EMD) together with Hilbert Transform to analyze precipitation time series over the Caspian Sea catchment. Several studies have shown that EMD can extract nonlinear and non-stationary signals better than Fast Fourier Transform (FFT) and Wavelet Transform. EMD decomposes the time series into a finite number of Intrinsic Mode Functions (IMFs) in the time-frequency domain, while FFT helps us operate either in the time or the frequency domain, which fuels limitations such as the inability of nonstationary signal processing and the lack of time transparency. Although Wavelet Transform is shown to be better than FFT, it fails to detect the instantaneous frequencies and needs to have prior information about characteristics of the data. On the other hand, EMD has shown that it is almost able to determine the signal characteristics with no previous assumptions to estimate the instantaneous frequencies of the signal. In this work, EMD is applied to identify the main frequencies of precipitation time series. Thereafter, a statistical procedure is used to identify the prominent IMF of the original signal.We use the correlation coefficient, Minkowski distance and variance test to extract the relevant and prominent IMFs. The results show that IMF 1–3 are the relevant components and are related to annual and biennial variations of precipitation time series over the Caspian catchment during 2003–2016, respectively.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

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