scholarly journals Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 211 ◽  
Author(s):  
Sandeep Kumar Patakamuri ◽  
Krishnaveni Muthiah ◽  
Venkataramana Sridhar
Keyword(s):  
Time Series ◽  
Serial Correlation ◽  
Andhra Pradesh ◽  
Statistical Tests ◽  
Monsoon Season ◽  
Rainfall Data ◽  
Change Points ◽  
Northeast Monsoon ◽  
Study Results ◽  
Independent Series

The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the subdistrict level and aggregated to monthly, annual, seasonal rainfall totals, and the number of rainy days. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. NonParametric Mann–Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann–Kendall tests (pre-whitening, trend-free pre-whitening, bias-corrected pre-whitening, and two variants of variance correction approaches) were applied. A significant increasing summer rainfall trend is observed in six out of 27 stations. Significant decreasing trends are observed at two stations during the southwest monsoon season and at two stations during the northeast monsoon season. To identify the trend change points in the time series, distribution−free cumulative sum test, and sequential Mann–Kendall tests were applied. Two open−source library packages were developed in R language namely, ”modifiedmk” and ”trendchange” to implement the statistical tests mentioned in this paper. The study results benefit water resource management, drought mitigation, socio−economic development, and sustainable agricultural planning in the region.

scholarly journals Long Term Homogeneity, Trend and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India

2019 ◽  
Author(s):  
Sandeep Kumar Patakamuri ◽  
Krishnaveni Muthiah ◽  
Venkataramana Sridhar
Keyword(s):  
Time Series ◽  
Serial Correlation ◽  
Andhra Pradesh ◽  
Water Resource Management ◽  
Statistical Tests ◽  
Monsoon Season ◽  
Rainfall Data ◽  
Change Points ◽  
South West Monsoon ◽  
Independent Series

Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the sub-district level and aggregated to monthly, annual and seasonal rainfall totals and the number of rainy days. The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. Non-Parametric Mann-Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann-Kendall tests (Pre-Whitening, Trend Free Pre-Whitening, Bias Corrected Pre-Whitening and two variants of Variance Correction Approaches) were applied. A significant increasing summer rainfall trend is observed in 6 out of 27 stations. Significant decreasing trends are observed at two stations in the south-west monsoon season and at two stations in the north-east monsoon season. To identify the trend change-points in the time series, distribution-free Cumulative SUM test and sequential Mann-Kendall tests were applied. Two open-source library packages were developed in R language namely, ‘modifiedmk’ and ‘trendchange’ to implement the statistical tests mentioned in this paper. The study will benefit water resource management, drought mitigation, socio-economic development and sustainable agricultural planning in the region.

scholarly journals Análise de não Homogeneidades de Séries de Vazão de Captações de Nascentes na Bacia Hidrográfica do Rio Gramame, PB, Brasil

2020 ◽  
Vol 13 (6) ◽  
pp. 2896
Author(s):  
Adriana Moura Martins ◽  
Hamilcar José Almeida Filgueira ◽  
Azamor Cirne de Azevedo Filho ◽  
Tarciso Cabral da Silva ◽  
Marcelo Henriques Da Silva Júnior
Keyword(s):  
Time Series ◽  
River Basin ◽  
Statistical Tests ◽  
Local Communities ◽  
Rainfall Data ◽  
South Coast ◽  
Change Points ◽  
Data Series ◽  
The South ◽  
Perennial Springs

A bacia hidrográfica do rio Gramame, no litoral sul paraibano, apresenta diversas nascentes perenes de água com vazões significativas que atendem a comunidades locais para diversos usos. Este trabalho teve como objetivo analisar quatro séries de vazões de captações de nascentes na região sudoeste da bacia e de dados pluviométricos, quanto à sua homogeneidade, entre os anos de 2010 e 2013. A questão motivadora da análise foi a suposta diminuição das vazões de captação das nascentes por consequência da construção de estradas e desmatamentos em áreas do entorno dessas nascentes. Para a análise da homogeneidade das séries, foram empregados testes estatísticos para determinação dos possíveis pontos de ruptura e de verificação da estacionariedade. Foi constatado que houve ruptura em todas as séries de vazões analisadas.  Analysis of non-homogeneities of time series of flow in sources in the Gramame River basin, Paraíba State, Brazil A B S T R A C TThe Gramame river basin on the south coast of Paraiba State, has several perennial springs with significant flows that serve local communities for various uses. However, the construction of roads, in areas around the springs, and recent deforestation indicated to have caused the decrease in flows captured from sources in the basin. This work aimed at analyzing four data series of flows captured from sources in the southwestern basin and the rainfall data series searching to verify their homogeneity, between the years 2010 and 2013. To analyze the homogeneity of the series, statistical tests were used to find significant change points and to verify the stationarity. It was found that rupture occurred in all series of flow analyzed.Keywords: flow from springs, hydrometeorological time series, groundwater.

scholarly journals Northeast monsoon rainfall and agricultural production in Tamilnadu and Andhra PradeshI - Rainfall variability and its significance in agricultural production

MAUSAM ◽  
2021 ◽  
Vol 64 (2) ◽  
pp. 309-316
Author(s):  
R.P. SAMUI ◽  
M.V. KAMBLE ◽  
J.P. SABALE
Keyword(s):  
Agricultural Production ◽  
Andhra Pradesh ◽  
Monsoon Rainfall ◽  
Positive Impact ◽  
Monsoon Season ◽  
Rainfall Variability ◽  
Southwest Monsoon ◽  
Positive Trend ◽  
Northeast Monsoon ◽  
Northeast Monsoon Rainfall

ekWulwu Hkkjrh; vFkZO;oLFkk dk egRoiw.kZ ?kVd gS tks —f"k dks izR;{k% :i  ls izHkkfor djrk gS D;ksafd ;g ,d pkSFkkbZ th-Mh-ih- vkSj —f"k ij fuHkZj 60 izfr’kr turk dh vkthfodk dks izHkkfor djrk gSA Hkkjr esa eq[;r% nf{k.k&if’peh ekWulwu +_rq ds nkSjku o"kkZ gksrh gSA Hkkjr esa vDrwcj ls fnlEcj ds nkSjku fo’ks"kdj iwohZ vkSj nf{k.kh jkT;ksa esa ekulwuksRrj vof/k] ftls mRrj iwohZ ekWulwu dgrs gS] esa Hkh dkQh o"kkZ gksrh gSA ;g o"kkZ —f"k ds fy, vkSj bu {ks=ksa ds lac) lsDVjksa ds fy, dkQh egRoiw.kZ gksrh gSA rfeyukMq ds iwohZ rVh; ftyksa esa nf{k.k ls mRrj rd o"kkZ esa o`f) dh izo`fRr  dk irk pyk gSA blds foijhr vka/kz izns’k ds rVh; nf{k.k iwohZ ftyksa esa vf/kd vkSj mRrj iwohZ Hkkxksa esa o"kkZ esa deh dh izo`fRr dk irk pyk gSA vka/kz izns’k dh vis{kk rfeyukMq esa o"kkZ dh vf/kdrk ds dkj.k mRrj iwohZ ekulwu dk nf{k.k if’pe ekulwu o"kkZ dh rqyuk esa —f"k mRiknu ij vf/kd izHkko dk irk pyrk gSA —f"k mRiknu ij o"kkZ ds izHkko ds v/;;uksa ls vka/kz izns’k esa pkoy vkSj eDdk ds mRiknu esa mRrj iwohZ ekWulwu ds ldkjkRed izHkko dk irk pyk gSA eDdk dh mit esa yxkrkj ldkjkRed izo`fRr dk irk pyk gSA mRrj iwohZ ekulwu _rq ds nkSjku vka/kz izns’k ds rVh; ftyksa dh rqyuk esa rfeyukMq ds rVh; ftyksa esa pØokr ;k vonkc dh otg ls Hkkjh ls vf/kd Hkkjh o"kkZ vkSj ck<+ ds dkj.k  mRiknu dh deh vkbZ gSA ifjofrZrk ds ckjs esa mfpr tkudkjh rFkk mRrj iwohZ ekulwu o"kkZ ds ekSleh iwokZuqeku ds lkFk&lkFk —f"k izpkyuksa ds fy, fofo/k uhfr;ksa dk fodkl djus ls bu {ks=ksa ds —f"k vkSj tylalk/ku lsDVjksa ds fy, fu.kZ; ysus esa egRoiw.kZ vuqiz;ksx Hkwfedk gksxhA  Monsoon which directly impacts agriculture is an important component of Indian economy because it influences about a quarter of the GDP and livelihood of 60% of the population who depend on agriculture for their livelihood. India receives rainfall mainly during southwest monsoon season. A considerable rainfall also occurs in India during the post monsoon period called as northeast monsoon during October to December, particularly over eastern and southern states and this is of great significance in agriculture and allied sectors in these regions.                 Increasing trend of rainfall is noticed from south to north in eastern coastal districts of Tamilnadu. On the contrary, it is higher in coastal southeast districts with decreasing trend in northeast parts of Andhra Pradesh. NE monsoon shows greater impact on agricultural production due to its higher quantum of rainfall compared to that of southwest monsoon rain in Tamilnadu than that in Andhra Pradesh. Studies on impact of rainfall on agricultural production revealed positive impact of NE monsoon on rice and maize production in AP. Maize yield is found to exhibit a consistent positive trend.  Loss in production due to heavy to very heavy rain and flooding associated with cyclone or depression was more prominent along the coastal districts of Tamilnadu than that in the coastal districts of Andhra Pradesh during northeast monsoon season.                 Proper understanding of the variability and developing diversified strategies for agricultural operations alongwith the seasonal prediction of northeast monsoon rainfall would have considerable application value for decision making in agriculture and water resource sectors of these regions.

How important is accounting for serial correlation and field significance in trend detection of extreme rainfall occurrences? 

2021 ◽  
Author(s):  
Stefano Farris ◽  
Roberto Deidda ◽  
Francesco Viola ◽  
Giuseppe Mascaro
Keyword(s):  
Time Series ◽  
Serial Correlation ◽  
Statistical Tests ◽  
Extreme Rainfall ◽  
Nonparametric Tests ◽  
Precipitation Extremes ◽  
Trend Detection ◽  
Monte Carlo Experiments ◽  
Precipitation Records

&lt;p&gt;A number of studies have shown that the ability of statistical tests to detect trends in hydrologic extremes is negatively affected by (i) the presence of autocorrelation in the time series, and (ii) field significance. Here, we investigate these two issues and evaluate the power of several trend tests using time series of frequencies (or counts) of precipitation extremes from long-term (100 years) precipitation records of 1087 gauges of the Global Historical Climate Network database. For this aim, we design several Monte Carlo experiments based on simulations of random count time series with different levels of autocorrelation and trend. We find the following. (1) The observed records are consistent with the hypothesis of autocorrelation induced by the presence of trends, indicating that the existence of serial correlation does not significantly affect trend detection. (2) Tests based on the linear and Poisson regressions are more powerful that nonparametric tests, such as Mann Kendall. (3) Accounting for field significance improves the interpretation of the results by limiting the rejection of the false null hypothesis. We then use these results to investigate the presence of trends in the observed records. We find that, depending on the quantiles used to define the frequency of precipitation extremes, 34-47% of the selected gages exhibit a statistically significant trend, of which 70-80% are positive and located mainly in United States and Northern Europe. The significant negative trends are mostly located in Southern Australia.&lt;/p&gt;

scholarly journals Role of Public Expenditure in Economic Growth: Econometric Evidence from Kosovo 2002–2015

2018 ◽  
Vol 6 (1) ◽  
pp. 74-87 ◽  
Author(s):  
Lekë Pula ◽  
Alban Elshani
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Public Expenditure ◽  
Positive Impact ◽  
Statistical Tests ◽  
Series Data ◽  
Total Budget ◽  
Study Results ◽  
The Impact ◽  
Budget Revenue

Abstract The aim of the study is to examine the impact of public expenditure on economic growth of Kosovo. Time series data span for the period of time 2002-2015. The structure of the econometric model is built on Keynesian theories and endogenous growth model. The model estimation is performed only after implementing the Augmented Dickey-Fuller (ADF) Unit Root test to estimate if time series are stationary. Several tests have been implemented to determine model validity. The model has met all the assumptions of statistical tests: error term residuals have a normal distribution (Jarque-Bera test), there is no auto-correlation between variables (Breusch-Godfrey Serial test), and error variances are constant, known as the principle of homoscedasticity (Breusch-Pagan-Godfrey test). Gross domestic product is used as a dependent variable in the model, while public expenditure (G), foreign direct investment (FDI), export (EXP) and total budget revenue (TrTax) are used as the endogenous variables. The study results have revealed that there is a positive and statistically significant effect of public expenditures and exports on economic growth. Total budget revenue has a positive impact on economic growth but this has not been proved to be statistically significant. The authors of the research have also found out that FDI is negative and statistically insignificant.

scholarly journals Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1633
Author(s):  
Elena-Simona Apostol ◽  
Ciprian-Octavian Truică ◽  
Florin Pop ◽  
Christian Esposito
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Change Point ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Prediction And Forecasting ◽  
Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

scholarly journals Assessment of Online Deliberative Quality: New Indicators Using Network Analysis and Time-Series Analysis

2021 ◽  
Vol 13 (3) ◽  
pp. 1187
Author(s):  
Bokyong Shin ◽  
Mikko Rask
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Collective Intelligence ◽  
Series Data ◽  
Online Data ◽  
Series Analysis ◽  
Study Results ◽  
Online Deliberation

Online deliberation research has recently developed automated indicators to assess the deliberative quality of much user-generated online data. While most previous studies have developed indicators based on content analysis and network analysis, time-series data and associated methods have been studied less thoroughly. This article contributes to the literature by proposing indicators based on a combination of network analysis and time-series analysis, arguing that it will help monitor how online deliberation evolves. Based on Habermasian deliberative criteria, we develop six throughput indicators and demonstrate their applications in the OmaStadi participatory budgeting project in Helsinki, Finland. The study results show that these indicators consist of intuitive figures and visualizations that will facilitate collective intelligence on ongoing processes and ways to solve problems promptly.

scholarly journals Synoptic Preconditions for Extreme Flooding during the Summer Asian Monsoon in the Mumbai Area

2014 ◽  
Vol 15 (1) ◽  
pp. 229-242 ◽  
Author(s):  
Marco Lomazzi ◽  
Dara Entekhabi ◽  
Joaquim G. Pinto ◽  
Giorgio Roth ◽  
Roberto Rudari
Keyword(s):  
Time Series ◽  
South Asian ◽  
Asian Monsoon ◽  
Indian Subcontinent ◽  
Monsoon Season ◽  
Value Added ◽  
South Asian Monsoon ◽  
Flood Events ◽  
The South ◽  
Synoptic Conditions

Abstract The summer monsoon season is an important hydrometeorological feature of the Indian subcontinent and it has significant socioeconomic impacts. This study is aimed at understanding the processes associated with the occurrence of catastrophic flood events. The study has two novel features that add to the existing body of knowledge about the South Asian monsoon: 1) it combines traditional hydrometeorological observations (rain gauge measurements) with unconventional data (media and state historical records of reported flooding) to produce value-added century-long time series of potential flood events and 2) it identifies the larger regional synoptic conditions leading to days with flood potential in the time series. The promise of mining unconventional data to extend hydrometeorological records is demonstrated in this study. The synoptic evolution of flooding events in the western-central coast of India and the densely populated Mumbai area are shown to correspond to active monsoon periods with embedded low pressure centers and have far-upstream influences from the western edge of the Indian Ocean basin. The coastal processes along the Arabian Peninsula where the currents interact with the continental shelf are found to be key features of extremes during the South Asian monsoon.

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