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Eastern Mediterranean University (2015)

Statistic and Probabilistic Variations and Rainfall Predictions of TRNC

Amjadi, Mohammadbagher

Titre : Statistic and Probabilistic Variations and Rainfall Predictions of TRNC

Auteur : Amjadi, Mohammadbagher

Université de soutenance : Eastern Mediterranean University

Grade : Master of Science in Civil Engineering 2015

This thesis deals with the monthly rainfall of six meteorological regions and TRNC (North Cyprus) as a whole for the hydrologic years from September 1975 to August 2014 period. In order to study these gathered monthly data statistically, other than the minimum required sample sizes for each region, the quality check tests (homogeneity, consistency, normality, independency, stationarity and trend) were as well carried out based on different parametric and/or non-parametric tests. To determine the most representative probability distribution models among the two widely used Normal and Log-Normal distributions for each region were use, since the gathered raınfall was based on monthly averages. In order to predict 5 years ahead of the yearly rainfall of each meteorological region and TRNC, three different time series models (Markov, Auto-regressive (AR) and Holt-Winter Multiplicative) were used. For this reason, the rainfall of hydrologic years from 1975-76 to 2003-04 were used for training and from 2004-05 to 2013-14 were used for forecasting (testing) the trained data. The best representative time-series model for each region was selected based on the standardized averages of four statistical error checking measures (MAPE, MAD, MSE and RMSE). The selected model for each region was then used to predict (estimate) the rainfall for five successive hydrologic years ahead from 2014-15 to 2018-19. To investigate the wetness or dryness characteristics of each regions and TRNC (North Cyprus), the hydrologic yearly averaged and the common monthly (from September to May) rainfall data sets were studied separately. Interestingly for all the months of all the regions, the dryness was controlling. Key words : rainfall, forecasted data, time series models, TRNC, wet or dry spells.


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