Trend analysis and change point determination for hydro-meteorological and groundwater data of Kizilirmak basin

2021 ◽  
Author(s):  
Hatice Citakoglu ◽  
Necmiye Minarecioglu
Keyword(s):  
Trend Analysis ◽  
Change Point ◽  
Point Determination ◽  
Kızılırmak Basin

Climate change effects on annual streamflow of Filyos River (Turkey)

2018 ◽  
Vol 11 (2) ◽  
pp. 420-433 ◽  
Author(s):  
Adem Yavuz Sönmez ◽  
Semih Kale
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Change Point ◽  
Change Point Analysis ◽  
Climatic Data ◽  
Climatic Parameters ◽  
Annual Streamflow ◽  
Climate Change Effects ◽  
Temperature And Precipitation ◽  
Point Analysis

Abstract The main purpose of this study was to estimate possible climate change effects on the annual streamflow of Filyos River (Turkey). Data for annual streamflow and climatic parameters were obtained from streamflow gauging stations on the river and Bartın, Karabük, Zonguldak meteorological observation stations. Time series analysis was performed on 46 years of annual streamflow data and 57 years of annual mean climatic data from three monitoring stations to understand the trends. Pettitt change-point analysis was applied to determine the change time and trend analysis was performed to forecast trends. To reveal the relationship between climatic parameters and streamflow, correlation tests, namely, Spearman's rho and Kendall's tau were applied. The results of Pettitt change-point analysis pointed to 2000 as the change year for streamflow. Change years for temperature and precipitation were detected as 1997 and 2000, respectively. Trend analysis results indicated decreasing trends in the streamflow and precipitation, and increasing trend in temperature. These changes were found statistically significant for streamflow (p < 0.05) and temperature (p < 0.01). Also, a statistically significant (p < 0.05) correlation was found between streamflow and precipitation. In conclusion, decreasing precipitation and increasing temperature as a result of climate change initiated a decrease in the river streamflow.

Trend analysis and change point detection of seasonal and annual precipitation in Iran

2019 ◽  
Vol 40 (1) ◽  
pp. 308-323 ◽  
Author(s):  
Somayeh Salehi ◽  
Majid Dehghani ◽  
Sayed M. Mortazavi ◽  
Vijay P. Singh
Keyword(s):  
Trend Analysis ◽  
Change Point ◽  
Change Point Detection ◽  
Annual Precipitation ◽  
Point Detection

Trend analysis and forecasting of the Gökırmak River streamflow (Turkey)

2020 ◽  
Vol 49 (3) ◽  
pp. 230-246 ◽  
Author(s):  
Gökhan Arslan ◽  
Semih Kale ◽  
Adem Yavuz Sönmez
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Change Point ◽  
Moving Average ◽  
Arima Model ◽  
Change Point Analysis ◽  
Point Analysis ◽  
Monthly Streamflow ◽  
Model Time Series ◽  
Streamflow Trend

AbstractThe objective of this paper is to determine the trend and to estimate the streamflow of the Gökırmak River. The possible trend of the streamflow was forecasted using an autoregressive integrated moving average (ARIMA) model. Time series and trend analyses were performed using monthly streamflow data for the period between 1999 and 2014. Pettitt’s change point analysis was employed to detect the time of change for historical streamflow time series. Kendall’s tau and Spearman’s rho tests were also conducted. The results of the change point analysis determined the change point as 2008. The time series analysis showed that the streamflow of the river had a decreasing trend from the past to the present. Results of the trend analysis forecasted a decreasing trend for the streamflow in the future. The decreasing trend in the streamflow may be related to climate change. This paper provides preliminary knowledge of the streamflow trend for the Gökırmak River.

scholarly journals Change Point Determination for an Attribute Process Using an Artificial Neural Network-Based Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yuehjen E. Shao ◽  
Ke-Shan Lin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Change Point ◽  
Vital Role ◽  
Statistical Process ◽  
Identification Approach ◽  
Point Determination ◽  
Mathematical Difficulties ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The change point identification has played a vital role in process improvement for an attribute process. This identification is able to effectively help process personnel to quickly determine the corresponding root causes and significantly improve the underlying process. Although many studies have focused on identifying the change point of a process, a generic identification approach has not been developed. The typical maximum likelihood estimator (MLE) approach has limitations: particularly, the known prior process distribution and mathematical difficulties. These deficiencies are commonly encountered in practice. Accordingly, this study proposes an artificial neural network (ANN) mechanism to overcome the difficulties of typical MLE approach in determining the change point of an attribute process. Specifically, the performance among the statistical process control (SPC) chart alone, the typical MLE approach, and the proposed ANN mechanism are investigated for the following cases: (1) a known attribute process distribution with the associated MLE being available to be used, (2) an unknown attribute process distribution with the MLE being unable to be used, and (3) an unknown attribute process distribution with the MLE being misused. The superior results and the performance of the proposed approach are reported and discussed.

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