A Comparative Effectiveness of Hierarchical and Non-hierarchical Regionalisation Algorithms in Regionalising the Homogeneous Rainfall Regions

2022 ◽  
Vol 30 (1) ◽  
pp. 319-342
Author(s):  
Zun Liang Chuan ◽  
Wan Nur Syahidah Wan Yusoff ◽  
Azlyna Senawi ◽  
Mohd Romlay Mohd Akramin ◽  
Soo-Fen Fam ◽  
...  
Keyword(s):  
Time Series Data ◽  
Rain Gauge ◽  
Series Data ◽  
Accurate Estimation ◽  
Spatial And Temporal Variability ◽  
Rainfall Time Series ◽  
Descriptive Data ◽  
Significant Difference ◽  
Quantile Estimates ◽  
Processing Techniques

Descriptive data mining has been widely applied in hydrology as the regionalisation algorithms to identify the statistically homogeneous rainfall regions. However, previous studies employed regionalisation algorithms, namely agglomerative hierarchical and non-hierarchical regionalisation algorithms requiring post-processing techniques to validate and interpret the analysis results. The main objective of this study is to investigate the effectiveness of the automated agglomerative hierarchical and non-hierarchical regionalisation algorithms in identifying the homogeneous rainfall regions based on a new statistically significant difference regionalised feature set. To pursue this objective, this study collected 20 historical monthly rainfall time-series data from the rain gauge stations located in the Kuantan district. In practice, these 20 rain gauge stations can be categorised into two statistically homogeneous rainfall regions, namely distinct spatial and temporal variability in the rainfall amounts. The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan- Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Furthermore, FKNH, HKNH, and LKNH yielded the highest regionalisation accuracy compared to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Based on the regionalisation results yielded in this study, the reliability and accuracy that assessed the risk of extreme hydro-meteorological events for the Kuantan district can be improved. In particular, the regional quantile estimates can provide a more accurate estimation compared to at-site quantile estimates using an appropriate statistical distribution.

scholarly journals Moderate-Intensity Physical Exercise Affects the Exercise Performance and Gut Microbiota of Mice

2021 ◽  
Vol 11 ◽  
Author(s):  
Wenqian Yang ◽  
Yuqian Liu ◽  
Guang Yang ◽  
Binglin Meng ◽  
Zhicheng Yi ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Exercise Performance ◽  
Exercise Intervention ◽  
Time Series Data ◽  
Shannon Index ◽  
Series Data ◽  
Moderate Intensity ◽  
Rrna Gene ◽  
Exercise Tests ◽  
Significant Difference

The gut microbiota is closely associated with the health of the host and is affected by many factors, including exercise. In this study, we compared the gut microbial changes and exercise performance over a 14-week period in mice that performed exercise (NE; n = 15) and mice that did not perform exercise (NC; n = 15). Mice were subjected to stool collection and exercise tests one week prior to adaptive training and after 2, 6, 10, and 14 weeks of exercise. Bacteria associated with the stool samples were assessed via Illumina-based 16S rRNA gene sequencing. While there was no significant difference in body weight between the groups (p > 0.05), the NE group had a significantly higher exercise performance from weeks 2–14 (p < 0.01) and lower fat coefficient (p < 0.01) compared with the NC group. The Shannon index of the gut microbiota in the NC group was higher than that in the NE group at weeks 6 and 10, and the Chao1 index was higher than that in the NE group at week 14. Exercise performance positively correlated with the relative abundance of Phascolarctobacterium. Grouped time series data analysis demonstrated that Bifidobacteria, Coprococcus, and one unnamed genus in the Clostridiales order were significantly increased in the NE group, which correlated with increased glucose, flavonoid, arginine, and proline metabolism. In conclusion, moderate-intensity treadmill exercise significantly increased the exercise performance of mice and changed the core bacteria and bacterial metabolic activity. These results provide a reference for studying the effects of exercise intervention and exercise performance on the gut microbiota of mice.

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.

The Triple Entente and the Triple Alliance 1880–1914: A Collective Goods Approach

1990 ◽  
Vol 84 (4) ◽  
pp. 1197-1206 ◽  
Author(s):  
John A. C. Conybeare ◽  
Todd Sandler
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Generalized Least Squares ◽  
Series Data ◽  
Accurate Estimation ◽  
Product Model ◽  
Public And Private ◽  
Static Tests ◽  
Joint Product ◽  
Triple Entente

The distribution of burdens in alliances may be explained in terms of public and private outputs. A joint product model is applied to the Triple Alliance and Triple Entente, using generalized least squares-auto regressive moving average (GLS-ARMA) techniques and time series data. Results indicate that countries regarded allies' military effort more as complements than as substitutes, though several examples of free-riding behavior are noted. The method used here may provide more accurate estimation of publicness problems than is found in the usual static tests.

scholarly journals Magnetospheric chaos and dynamical complexity response during storm time disturbance

2020 ◽  
Author(s):  
Irewola Aaron Oludehinwa ◽  
Olasunkanmi Isaac Olusola ◽  
Olawale Segun Bolaji ◽  
Olumide Olayinka Odeyemi ◽  
Abdullahi Ndzi Njah
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Time Series Data ◽  
Geomagnetic Storms ◽  
Approximate Entropy ◽  
Series Data ◽  
Dynamical Complexity ◽  
Significant Difference ◽  
Dynamical Features

Abstract. In this study, we examine the magnetospheric chaos and dynamical complexity response in the disturbance storm time (Dst) and solar wind electric field (VBs) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). The time series data of the Dst and VBs are analyzed for the period of nine years using nonlinear dynamics tools (Maximal Lyapunov Exponent, MLE, Approximate Entropy, ApEn and Delay Vector Variance, DVV). We found a significant trend between each nonlinear parameter and the categories of geomagnetic storm. The MLE and ApEn values of the Dst indicate that chaotic and dynamical complexity response are high during minor geomagnetic storms, reduce at moderate geomagnetic storms and declined further during major geomagnetic storms. However, the MLE and ApEn values obtained in VBs indicate that chaotic and dynamical complexity response are high with no significant difference between the periods that are associate with minor, moderate and major geomagnetic storms. The test for nonlinearity in the Dst time series during major geomagnetic storm reveals the strongest nonlinearity features. Based on these findings, the dynamical features obtained in the VBs as input and Dst as output of the magnetospheric system suggest that the magnetospheric dynamics is nonlinear and the solar wind dynamics is consistently stochastic in nature.

scholarly journals A systematic review of data pre-processing methods and unsupervised mining methods used in profiling smart meter data

2021 ◽  
Vol 5 (4) ◽  
pp. 284-314
Author(s):  
Folasade M. Dahunsi ◽  
◽  
Abayomi E. Olawumi ◽  
Daniel T. Ale ◽  
Oluwafemi A. Sarumi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Time Series Data ◽  
Series Data ◽  
Smart Meters ◽  
Data Mining Technique ◽  
Intrinsic Nature ◽  
Hidden Knowledge ◽  
Mining Methods ◽  
Processing Techniques

<abstract> <p>The evolution of smart meters has led to the generation of high-resolution time-series data - a stream of data capable of unveiling valuable knowledge from consumption behaviours for different applications. The ability to extract hidden knowledge from such massive amounts of data requires that it be analysed intelligently. Hence, for a clear representation of the various consumption behaviours of consumers, a good number of data mining technologies are usually employed. This paper presents a systematic review of the various data mining techniques and methodologies employed while profiling energy data streams. The review identifies the strengths and shortcomings of existing data mining methods as applied in research, focusing more on data processing techniques and load clustering. Also discussed are data mining methods used to profile consumption data, their pros and cons. It was inferred during the research that the choice of data mining technique employed is highly dependent on the application it is intended for and the intrinsic nature of the dataset.</p> </abstract>

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 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.

Determinants of Foreign Direct Investment in Pakistan: A Comparative Analysis in Democratic and Non-Democratic Eras

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
YASIR KHAN ◽  
ALAM REHMAN ◽  
FARMAN ULLAH KHAN
Keyword(s):  
Foreign Direct Investment ◽  
Direct Investment ◽  
Global Economy ◽  
Time Series Data ◽  
Trade Openness ◽  
Series Data ◽  
Dummy Variable ◽  
Unit Root Testing ◽  
Long Run ◽  
Significant Difference

The chief objective of this research is to investigate empirically the determinants which affect Foreign Direct Investment (FDI) inflow in Democratic and non-Democratic eras of Pakistan by using yearly data from 1980 -2014. In this research, six independent variables have been taken along with Dummy which are Gross Domestic Product, Interest Rate, Trade Openness, Inflation Rate, Exchange rate, Dummy variable and one dependant variable which is Foreign Direct Investment. For econometric analysis, annual time series data were collected from the Pakistan Bureau of Statistics, UNCTAD, World Development Indicator (various issues), International Financial Statistics, Global Economy and Economic Survey of Pakistan. This thesis applied advanced econometric methodology which comprises unit root testing and Johansen co-integration analysis. The Vector Error Correction Model (VECM) was employed to identify both the long-run and short run relationship between FDI and its determinants. The dummy variable captured the difference as there is a significant difference between the determinants of Foreign direct investment in Democratic and Non-Democratic eras of Pakistan.

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