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Rainfall and IDF data of Darwin were collected from Australian Bureau of Meteorology (2012).
In Qatar, situated in an arid region, the existing IDF data were developed in 1991 using a limited data set.
The interpolated extreme rainfall statistics, using no data transformation, have been used by the Australian Bureau of Meteorology to produce new IDF data for the Australian continent.
This paper presents the development of new IDF data for the State of Qatar using the method of L-moments and the index regional frequency analysis approach.
They performed frequency analysis of disaggregated data to establish IDF curves for 2, 5, 10, 25 and 50 years return periods and found that 95%% confidence interval boundaries of the generalized extreme values (GEV) fit 6 h observed IDF data.
***Authors' calculation based on IDF data.
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The LSA transforms the high dimensional TF-IDF data matrix (A) into a low-dimensional latent space through a mathematical procedure known as singular value decomposition (SVD) [ 18].
The TF-IDF data matrix (A) is first normalized across the rows by dividing frequency of the word in each document by the highest frequency of that word in all the documents.
The SVD is a matrix factorization technique that decomposes the TF-IDF matrix into three different matrices as shown in Eq. 2. (2) A = U S V T. The first s Eigen vectors are considered for mapping the high-dimensional TF-IDF data matrix (A) to the low-dimensional space as shown in the Eq. 3. (3) A ≈ U S S S V S T.
However, compared to RG-IDF curves, IDF curves based on simulated precipitation data of MM5 are overestimated especially for IDF curves of 2-year return period.
To examine these issues and to compare the ability of the metabolic syndrome to predict the risk of developing diabetes using two definitions, namely those of the National Cholesterol Education Program (NCEP) and IDF, we used data from a large prospective German study.
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