A composite index for drought hazard assessment using CPC rainfall time series data

2017 ◽  
Vol 14 (9) ◽  
pp. 1981-1988 ◽  
Author(s):  
C. S. Murthy ◽  
J. Singh ◽  
P. Kumar ◽  
M. V. R. Sesha Sai
Keyword(s):  
Time Series ◽  
Hazard Assessment ◽  
Time Series Data ◽  
Composite Index ◽  
Series Data ◽  
Rainfall Time Series ◽  
Drought Hazard

Congo Basin's Shrinking Watersheds

2020 ◽  
pp. 1452-1468
Author(s):  
Bila-Isia Inogwabini
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Democratic Republic Of Congo ◽  
Congo Basin ◽  
Series Data ◽  
Water Volume ◽  
Rainfall Time Series ◽  
Aquatic Resources ◽  
Basin Water

Rainfall time series data from three sites (Kinshasa, Luki, and Mabali) in the western Democratic Republic of Congo were analyzed using regression analysis; rainfall intensities decreased in all three sites. The Congo Basin waters will follow the equation y = -20894x + 5483.16; R2 = 0.7945. The model suggests 18%-loss of the Congo Basin water volume and 7%-decrease for fish biomasses by 2025. Financial incomes generated by fishing will decrease by 11% by 2040 compared with 1998 levels. About 51% of women (N= 408,173) from the Lake Tumba Landscape fish; their revenues decreased by 11% between 2005 and 2010. If this trend continues, women's revenues will decrease by 59% by 2040. Decreased waters will severely impact women (e.g. increasing walking distances to clean waters). Increasing populations and decreasing waters will lead to immigrations to this region because water resources will remain available and highly likely ignite social conflicts over aquatic resources.

scholarly journals Modified singular spectrum analysis in identifying rainfall trend over peninsular Malaysia

2019 ◽  
Vol 15 (1) ◽  
pp. 283
Author(s):  
S.M. Shaharudin ◽  
N. Ahmad ◽  
N.H. Zainuddin
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Singular Values ◽  
Peninsular Malaysia ◽  
Series Data ◽  
Rainfall Time Series ◽  
Singular Spectrum ◽  
Torrential Rainfall

<p>Identifying the local time scale of the torrential rainfall pattern through Singular Spectrum Analysis (SSA) is useful to separate the trend and noise components. However, SSA poses two main issues which are torrential rainfall time series data have coinciding singular values and the leading components from eigenvector obtained from the decomposing time series matrix are usually assesed by graphical inference lacking in a specific statistical measure. In consequences to both issues, the extracted trend from SSA tended to flatten out and did not show any distinct pattern.  This problem was approached in two ways. First, an Iterative Oblique SSA (Iterative O-SSA) was presented to make adjustment to the singular values data. Second, a measure was introduced to group the decomposed eigenvector based on Robust Sparse K-means (RSK-Means). As the results, the extracted trend using modification of SSA appeared to fit the original time series and looked more flexible compared to SSA.</p>

scholarly journals Analisis Indeks Harga Saham Gabungan (IHSG) Menggunakan Metode Wavelet Thresholding

2019 ◽  
Vol 8 (3) ◽  
Author(s):  
Zahra Awaliya Fauziah ◽  
Junaidi ◽  
Lilies Handayani
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Composite Index ◽  
Series Data ◽  
Wavelet Thresholding ◽  
Wavelet Filters ◽  
Threshold Functions ◽  
Forecasting Method ◽  
Thresholding Function

Stock is one type of long-term investment in the capital market. The stock movement indicator that is most often used in analysis by investors is the  Indonesia Composite Index (ICI). ICI data is a variety of time series data, so it can be analyzed using forecasting. One forecasting method that can be used is the wavelet thresholding method. The wavelet threshold can analyze stationary, non-stationary, and nonlinear time series data by producing smooth estimates. The wavelet threshold has a wavelet filter and threshold parameters and threshold functions that can be used in analyzing. In this study MSE was assessed from several wavelet filters namely haar, daubechies, and coiflets filters at levels 1 to 7 with the thresholding function namely soft thresholding and thresholding parameters, namely minimax thresholding and sure thresholding. The data used is IHGS data in 2018 totaling 240 data. Based on the data analysis performed, MSE was obtained which means that the best filter provided in order 2 wavelet coiflet filter at level 2 and thresholding parameter is sure of thresholding with MSE value of 0.0094

scholarly journals Trend Analysis of Rainfall Time Series in Shanxi Province, Northern China (1957–2019)

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2335
Author(s):  
Feng Gao ◽  
Yunpeng Wang ◽  
Xiaoling Chen ◽  
Wenfu Yang
Keyword(s):  
Time Series ◽  
Water Supply ◽  
Trend Analysis ◽  
Agricultural Production ◽  
Time Series Data ◽  
Shanxi Province ◽  
Series Data ◽  
Rainfall Time Series ◽  
Mk Test ◽  
Wutai Shan

Changes in rainfall play an important role in agricultural production, water supply and management, and social and economic development in arid and semi-arid regions. The objective of this study was to examine the trend of rainfall series from 18 meteorological stations for monthly, seasonal, and annual scales in Shanxi province over the period 1957–2019. The Mann–Kendall (MK) test, Spearman’s Rho (SR) test, and the Revised Mann–Kendall (RMK) test were used to identify the trends. Sen’s slope estimator (SSE) was used to estimate the magnitude of the rainfall trend. An autocorrelation function (ACF) plot was used to examine the autocorrelation coefficients at various lags in order to improve the trend analysis by the application of the RMK test. The results indicate remarkable differences with positive and negative trends (significant or non-significant) depending on stations. The largest number of stations showing decreasing trends occurred in March, with 10 out of 18 stations at the 10%, 5%, and 1% levels. Wutai Shan station has strong negative trends in January, March, April, November, and December at the level of 1%. In addition, Wutai Shan station also experienced a significant decreasing trend over four seasons at a significance level of 1% and 10%. On the annual scale, there was no significant trend detected by the three identification methods for most stations. MK and SR tests have similar power for detecting monotonic trends in rainfall time series data. Although similar results were obtained by the MK/SR and RMK tests in this study, in some cases, unreasonable trends may be provided by the RMK test. The findings of this study could benefit agricultural production activities, water supply and management, drought monitoring, and socioeconomic development in Shanxi province in the future.

Congo Basin's Shrinking Watersheds

2017 ◽  
pp. 211-226
Author(s):  
Bila-Isia Inogwabini
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Time Series Data ◽  
Democratic Republic Of Congo ◽  
Congo Basin ◽  
Series Data ◽  
Water Volume ◽  
Rainfall Time Series ◽  
Aquatic Resources ◽  
Basin Water

Rainfall time series data from three sites (Kinshasa, Luki, and Mabali) in the western Democratic Republic of Congo were analyzed using regression analysis; rainfall intensities decreased in all three sites. The Congo Basin waters will follow the equation y = -20894x + 5483.16; R2 = 0.7945. The model suggests 18%-loss of the Congo Basin water volume and 7%-decrease for fish biomasses by 2025. Financial incomes generated by fishing will decrease by 11% by 2040 compared with 1998 levels. About 51% of women (N= 408,173) from the Lake Tumba Landscape fish; their revenues decreased by 11% between 2005 and 2010. If this trend continues, women's revenues will decrease by 59% by 2040. Decreased waters will severely impact women (e.g. increasing walking distances to clean waters). Increasing populations and decreasing waters will lead to immigrations to this region because water resources will remain available and highly likely ignite social conflicts over aquatic resources.

Use of Hydrologic Time Series Data for Analysis the Karstic Drought Characteristic

2014 ◽  
Vol 641-642 ◽  
pp. 127-131
Author(s):  
Li Hong Liu ◽  
Da Sheng Wang ◽  
He Huang ◽  
Guang Quan Xu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Water Volume ◽  
Significant Heterogeneity ◽  
Drought Duration ◽  
Hydrologic Time Series ◽  
Drought Hazard ◽  
Groundwater Drought ◽  
Drought Frequency Analysis

Karstification creates significant heterogeneity of hydraulic conductivity within the aquifer, where flows are organized to a hierarchical structure, from the surface to the spring. A karstic aquifer subjected to groundwater flood and drought, as a site for the occurrence of karst groundwater, is the main or unique focus for groundwater development and utilization in southwestern China. The present paper introduces a methodology devoted to groundwater drought hazard assessment. It focuses on groundwater drought by applying of the spring time series for an estimate and categorization of operating resources of groundwater. The results show that a permit for use of water for ER1+ER2 up to 0.48 m3/s, with the exceeding probability 80% selected for representing dry. The longest drought duration time was happened in the year 1993 with the 2.9×106m3 shortage of water volume. Groundwater drought frequency analysis provides a useful tool for water management.

scholarly journals Enhanced accuracy of rainfall–runoff modeling with wavelet transform

2012 ◽  
Vol 15 (2) ◽  
pp. 392-404 ◽  
Author(s):  
Chien-ming Chou
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Response Function ◽  
Time Series Data ◽  
Series Data ◽  
Rainfall Time Series ◽  
Calibration Data ◽  
Validation Data ◽  
Original Scale ◽  
Using Data

Wavelet transform (WT) is typically used to decompose time series data for only one hydrological feature at a time. This study applied WT for simultaneous decomposition of rainfall and runoff time series data. For the calibration data, the decomposed rainfall and runoff time series calibrate the subsystem response function using the least squares (LS) method at each scale. For the validation data, the decomposed rainfall time series are convoluted with the estimated subsystem response function to obtain the estimated runoff at each scale. The estimated runoff at the original scale can be obtained by wavelet reconstruction. The efficacy of the proposed method is evaluated in two case studies of the Feng-Hua Bridge and Wu-Tu watershed. The analytic results confirm that the proposed wavelet-based method slightly outperforms the conventional method of using data only at the original scale. The results also show that the runoff hydrograph estimated by using the proposed method is smoother than that obtained using a single scale.

scholarly journals Processing of Rainfall Time Series Data in the State of Rio de Janeiro

2021 ◽  
Vol 4 (4) ◽  
Author(s):  
Givanildo De Gois ◽  
José Francisco De Oliveira-Júnior
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
The State ◽  
Data Consistency ◽  
Quality Data ◽  
Series Data ◽  
Time Variability ◽  
Rainfall Time Series ◽  
Raw Data ◽  
State Data

The goal was to perform the filling, consistency and processing of the rainfall time series data from 1943 to 2013 in five regions of the state. Data were obtained from several sources (ANA, CPRM, INMET, SERLA and LIGHT), totaling 23 stations. The time series (raw data) showed failures that were filled with data from TRMM satellite via 3B43 product, and with the climatological normal from INMET. The 3B43 product was used from 1998 to 2013 and the climatological normal over the 1947- 1997 period. Data were submitted to descriptive and exploratory analysis, parametric tests (Shapiro-Wilks and Bartlett), cluster analysis (CA), and data processing (Box Cox) in the 23 stations. Descriptive analysis of the raw data consistency showed a probability of occurrence above 75% (high time variability). Through the CA, two homogeneous rainfall groups (G1 and G2) were defined. The group G1 and G2 represent 77.01% and 22.99% of the rainfall occurring in SRJ, respectively. Box Cox Processing was effective in stabilizing the normality of the residuals and homogeneity of variance of the monthly rainfall time series of the five regions of the state. Data from 3B43 product and the climatological normal can be used as an alternative source of quality data for gap filling.

scholarly journals Identifikasi Model I-Garch (Integrated Generalized Autoregressive Conditionally Heterocedastic) Untuk Peramalan Value At Risk

2020 ◽  
Vol 3 (1) ◽  
pp. 25-38
Author(s):  
Nendra Mursetya Somasih Dwipa
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Confidence Level ◽  
Value At Risk ◽  
Time Series Data ◽  
Composite Index ◽  
Information Criteria ◽  
Stock Index ◽  
Series Data ◽  
Residual Variance

A stock returns data are one of type time series data who has a high volatility and different variance in every point of time. Such data are volatile, seting up a pattern of asymmetrical, having a nonstationary model, and that does not have a constant residual variance (heteroscedasticity). A time series ARCH and GARCH model can explain the heterocedasticity of data, but they are not always able to fully capture the asymmetric property of high frequency. Integrated Generalized Autoregresive Heteroskedascticity (IGARCH) model overcome GARCH weaknesses in capturing unit root. Furthermore IGARCH models were used to estimate the value of VaR as the maximum loss that will be obtained during a certain period at a certain confidence level. The aim of this study was to determine the best forecasting model of Jakarta Composite Index (JSI). The model had used in this study are ARCH, GARCH, and IGARCH. From the case studies were carried out, the result of forecasting volatility of stock index by using IGARCH(1,1) obtained log likelihood values that 3857,979 to the information criteria AIC = -6,3180; BIC = -6,3013; SIC = -6,3180; dan HQIC = -6,3117. Value of VaR movement of the JCI if it becomes greater the investment is Rp.500,000,000.00 with a confidence level of 95% on the date of July 2, 2015 using a model IGARCH (1,1) is Rp7.166.315,00.

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