K-S Test for Crack Increment in Probabilistic Fracture Mechanics Analysis

Akramin, M. R. M.; Takahashi, Atsushi; Husnain, M. N. M.; Chuan, Zun Liang

doi:10.1088/1755-1315/498/1/012035

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…If the maximum distance is less than a permitted tolerance, the referred type of distribution will be acceptable ( Akramin et al., 2020 ). Tolerance is a function of the null hypothesis that is described as a table in different sources ( Akramin et al., 2020 ). The Null hypothesis in this work was assumed to be 0.05 as previously suggested by Madadgar and Moradkhani (2012) .…”

Section: Methodsmentioning

confidence: 99%

“…The K-S test (Xiao, 2017;Zhao et al, J o u r n a l P r e -p r o o f 2017) is usually used to measure the difference among the CDF of forecast (or observations) data in the hindcast period and an empirical cumulative distribution (defined as a benchmark to investigate whether distribution looks exactly like the sample). If the maximum distance is less than a permitted tolerance, the referred type of distribution will be acceptable (Akramin et al, 2020). Tolerance is a function of the null hypothesis that is described as a table in different sources (Akramin et al, 2020).…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

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Copula based post-processing for improving the NMME precipitation forecasts

Yazdandoost

Zakipour

Izadi

2021

Heliyon

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Using reliable and timely precipitation forecasts on a monthly or seasonal scale could be useful in many water resources management planning, especially in countries facing drought challenges. Amongst many, the North American Multi-Model Ensemble (NMME) is one of the most well-known models. In this study, a Bayesian method based on Copula functions has been applied to improve NMME precipitation forecasts. This method is based on the existence of a correlation between the raw forecast and observational data. Two main factors affect the results of rainfall improvement based on the selected method. This research has presented innovative methods in these regards namely; 1) the approach of selecting the appropriate statistical distribution for variables and 2) the selection method of improved data according to the conditional probability distribution functions (CPDF). To evaluate the effectiveness of the statistical distribution, firstly the precipitation forecast improvement model has been developed based on the application of parametric (Exponential, Normal, Gamma, LogNormal and General Exreteme Value (GEV)) and non-parametric distributions (Standard Normal Kernel). Then the novel mixed distribution function based on GEV parametric distribution and Standard Normal Kernel (non-parametric distribution) has been suggested. As the second aim, a new method for selecting improved data based on the center of mass of estimated CPDF is presented. The evaluation of the proposed method for estimating the statistical distribution of data and improving the forecast precipitation by the NMME model has been performed in Sistan and Baluchestan province in Iran. In this regard, the data of 1982–2010 for the calibration period and the data of 2012–2016 for the validation of the results have been used. According to the results, the non-parametric distribution best fitted with the data in the time series and selecting the appropriate bandwidth increased the efficiency of this distribution. Besides, due to the weakness of non-parametric distributions in the boundaries, the use of GEV distribution with a high ability to estimate boundary conditions as semi-parametric distribution, led to improved performance of the proposed distribution. Finally, the selection of the improved data based on the center of the mass method has efficiently provided much improvement compared to the maximum likelihood method commonly used.

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Section: Methodsmentioning

confidence: 99%

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

Copula based post-processing for improving the NMME precipitation forecasts

Yazdandoost

Zakipour

Izadi

2021

Heliyon

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“…Kolmogorov-Smirnow (K-S) test is a non-parametric statistical test method for continuous distribution. This test is often used to compare whether a single sample conforms to a known distribution (compare the cumulative frequency distribution of the sample data with a specifi c theoretical distribution, and if the diff erence between the two is small, it is inferred that the sample is taken from a specifi c distribution cluster), the wo-sample K-S test compares the similarity of the cumulative distribution (continuous distribution) of the two datasets [16].…”

Section: (1)mentioning

confidence: 99%

Wind Power Prediction Based on Difference Method

Zhang¹,

Zhang²,

Cui³

2022

Journal of Compurters

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<p> The renewable wind power sources are difficult to be predicted in view of the fluctuating factors such as wind bearing, pressure, wind speed, and humidity of the surrounding atmosphere. An attempt is made in this paper to propose a difference method to build a neural network and a long short term memory (LSTM) model for wind power prediction. First, the correlation of each data is analyzed and then per-forming difference processing on the original data to solve the problem that the original data cannot be analyzed by probability distribution. The prediction is made by building the neural network and LSTM and feeding the original data and the difference-processed data into the neural network model respective-ly. Finally, the data are added for validation, and the raw data used include wind power data in Belgium from November 1, 2019 to November 30, 2019.The experimental results show that the LSTM prediction accuracy is improved by 178.67%, and is effective in predicting long-term wind power data with 216.06% accuracy improvement, the neural network prediction accuracy is improved by 154.07%, and the short-term wind power prediction accuracy is improved by 228%.</p> <p> </p>

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K-S Test for Crack Increment in Probabilistic Fracture Mechanics Analysis

Cited by 2 publications

References 6 publications

Copula based post-processing for improving the NMME precipitation forecasts

Copula based post-processing for improving the NMME precipitation forecasts

Wind Power Prediction Based on Difference Method

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