IDENTIFYING THE BEST-FIT PROBABILITY DISTRIBUTION MODEL  TO PREDICT THE ANNUAL MAXIMUM DAILY RAINFALL  IN DUHOK CITY, IRAQ

KHAMLEEN A.  MAHMOUD; SAYRAN A.  IBRAHIM; IMRAN N.  MOHSIN; ZAHRAA M.  KLARI

doi:10.26682/sjuod.2023.26.2.2

KHAMLEEN A. MAHMOUD Dept. of Mechanical Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq
SAYRAN A. IBRAHIM Dept. of Civil Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq
IMRAN N. MOHSIN Technical College of Engineering, Duhok Polytechnic University, Kurdistan Region-Iraq
ZAHRAA M. KLARI Dept. of Civil Engineering, College of Engineering, University of Duhok, Kurdistan Region-Iraq

DOI: https://doi.org/10.26682/sjuod.2023.26.2.2

Keywords: Rainfall, Probability Distribution, Annual Maximum Daily Rainfall, Goodness-Of-Fit Test

Abstract

Adequate knowledge of extreme events design for a long return period is required for the design and construction of such projects. This study aims to investigate the best-fit probability distribution model to predict extreme rainfall for the 30 years of observed annual maximum daily rainfall data of Duhok city. For the aim of this study, six candidate probability distribution methods were selected namely Normal, Log-Normal, Log-Normal 3p, Log-Pearson type 3, Generalized Extreme Value (GEV), and Gumbel Max. Then these models were subjected to the three goodness-of-fit tests: Chi-Square, Anderson-Darling and Kolmogorov tests. Depending on the lowest summation of ranked scores of each probability distribution model, in the area being researched, the best-fit distribution is chosen. According to the analysis of data, Generalized Extreme Value distribution can be used as best-fit prediction of the maximum daily rainfall in a year followed by Log-Normal 3P and Log-Pearson 3P for Duhok city. Furthermore, the maximum daily rainfall calculated values for the return periods of 2, 5, 10, 25, 50 and 100 years by the GEV distribution formula of return periods were found to be 70 mm, 93mm, 109mm, 130mm, 147 mm and 164mm respectively. The study's findings can be applied to the creation of more precise flood risk and damage models

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