Testing and improving type 1 error performance of Şen’s innovative trend analysis method

Alashan, Sadık

doi:10.1007/s00704-020-03363-5

Cited by 17 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results revealed that the P-W could reliably determine trends transported even in the serially dependent signals, especially in the original observations, which improve the accuracy of trends detection with periodic characteristics. Using some magnitude percentages, bootstraps, and variance correction based on Monte Carlo simulation for some rainfall England regions, Alashan (2020) concludes that the improved version ITM_R gives successful results compared with Mann-Kendall (MK) test. In addition, Nourani et al (2015) combined ITM with DWT.…”

Section: New Insights From the Proposed Hybrid Methodsmentioning

confidence: 98%

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

et al. 2022

View full text Add to dashboard Cite

In this paper, the Innovative Trend Methodology (ITM) and their inspired approaches, i.e., Double (D-ITM) and Triple (T-ITM), were combined with Hilbert Huang transform (HHT) time frequency-based method. The new hybrid methods (i.e., ITM-HHT, D-ITM-HHT, and T-ITM-HHT) were proposed and compared to the DWT-based methods in order to recommend the best method. Three total annual rainfall time series from 1920 to 2011 were selected from three hydrological basins in Northern Algeria. The new combined models (ITM-HHT, D-ITM-HHT, and T-ITM-HHT) revealed that the 1950–1975 period has significant wet episodes followed by a long-term drought observed in the western region of Northern Algeria, while Northeastern Algeria presented a wet period since 2001. The proposed approaches successfully detected, in a visible manner, hidden trends presented in the signals, which proves that the removal of some modes of variability from the original rainfall signals can increase the accuracy of the used approaches.

show abstract

Section: New Insights From the Proposed Hybrid Methodsmentioning

confidence: 98%

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The results for the MK test power concurred with findings from Wang et al which stated that the presence of autocorrelation misinterpreted the power of MK test [9] . Alashan also discovered that the MMK_ITA was more robust to serial correlation than the classical MK test [20].…”

Section: Power Of the Tests For Autocorrelated Time Seriesmentioning

confidence: 99%

“…Another trend detection method is known as modified Mann-Kendall supported by Sen's Innovative Trend Analysis (MMK_ITA) which was proposed by Alashan [20]. This test can be considered as the combination of rank-based and slope-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…The MMK_ITA tests uses the ITA method to calculate the trend slope instead of the SSE method (MMK_SSE) as the ITA method includes the contribution of extreme values to the trend component. Alashan concluded that MMK_ITA was better than other MK methods as it was more robust in the influence of serial correlation after applying the ITA, MK, MMK_SSE and MMK_ITA methods to monthly maximum temperature records in Oxford station, England [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of the Rank-based and Slope-based Nonparametric Tests for Trend Detection in Climate Time Series

Ali,

Abdul Ghani

2024

AMCI

View full text Add to dashboard Cite

Trend detection in climate time series data is crucial for understanding climate change, predicting future climate patterns, assessing impacts, managing resources, and formulating policies. Several trend detection methods have been introduced in the literature, including parametric and non-parametric approaches. Nonparametric trend detection methods are often considered more preferable than parametric methods in certain situations due to their flexibility and robustness. Comparing various nonparametric methods of trend detection is vital in data analysis because different techniques can yield divergent results based on the same dataset. In this study, three nonparametric trend tests which were the MannKendall (MK), Sen’s Innovative Trend Analysis (ITA) and Modified Mann-Kendall by Sen’s Innovative Trend Analysis (MMK_ITA) were compared based on their power. The MK test is a rank-based test and the ITA is a slope-based test. Meanwhile, the combination of rank-based and slope-based methods is known as the MMK_ITA test. The power analysis was conducted through Monte Carlo simulation on normal, non-normal and autocorrelated time series. The simulation results indicated that test power relied on magnitude of linear trend slope, sample sizes, distribution type and variation in time series. These tests were then applied to monthly maximum temperature from 2002 until 2021 for Selangor, Malaysia. This study found that the slope-based test performed better compared to the rank-based test and their combined methods from the simulation studies and real data application based on the calculated power.

show abstract

Standardized Innovative Polygon Trend Analysis for Climate Change Assessment (S-IPTA)

Alashan,

Abu Arra,

Şişman

2024

Pure Appl. Geophys.

View full text Add to dashboard Cite

Research and applications on trend analysis have recently been on the agenda and are top priorities in many disciplines due to the effects of climate change. After a thorough evaluation of the literature, it is noted that different hydro-meteorological variables, such as precipitation, temperature, etc., are studied and analyzed individually. This research proposes a new innovative polygon trend analysis application (S-IPTA) using the standardization concept to fill this gap in classical trend applications and comprehensively compare the trends of different variables to temporal and spatial patterns. Firstly, using statistical standardization, S-IPTA adjusts the original data sets and makes them dimensionless. Then, the innovative trend analyses are conducted and interpreted on one single graph (S-IPTA). The S-IPTA methodology is applied to monthly precipitation and temperature time series of Konya Basin in Türkiye at ten meteorological stations between 1959 and 2022. For precipitation, the S-IPTA did not exhibit a consistent polygon across all stations within the study area, while the temperature polygon was more regular, indicating that the temperature mean was generally stable with a positive trend. Also, S-IPTA shows the difference between the average value for each month and the newly proposed long-term average value (0). S-IPTA also provides a basis for a better interpretation of climate change and its effects by providing a common denominator for various trend characteristics, such as trend magnitudes and trend transitions in different hydro-meteorological time series.

show abstract

Testing and improving type 1 error performance of Şen’s innovative trend analysis method

Cited by 17 publications

References 28 publications

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

A Comparison of the Rank-based and Slope-based Nonparametric Tests for Trend Detection in Climate Time Series

Standardized Innovative Polygon Trend Analysis for Climate Change Assessment (S-IPTA)

Contact Info

Product

Resources

About