IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Flood Frequency Analysis Using Bayesian Paradigm: A Case Study From Pakistan

Flood Frequency Analysis Using Bayesian Paradigm: A Case Study From Pakistan
View Sample PDF
Author(s): Ishfaq Ahmad (International Islamic University, Pakistan), Alam Zeb Khan (International Islamic University, Pakistan), Mirza Barjees Baig (King Saud University, Saudi Arabia)and Ibrahim M. Almanjahie (King Khalid University, Saudi Arabia)
Copyright: 2020
Pages: 20
Source title: Decision Support Methods for Assessing Flood Risk and Vulnerability
Source Author(s)/Editor(s): Ahmed Karmaoui (Southern Center for Culture and Sciences (SCCS), Morocco)
DOI: 10.4018/978-1-5225-9771-1.ch005

Purchase

View Flood Frequency Analysis Using Bayesian Paradigm: A Case Study From Pakistan on the publisher's website for pricing and purchasing information.

Abstract

At-site flood frequency analysis (FFA) of extreme hydrological events under Bayesian paradigm has been carried out and compared with frequentist paradigm of maximum likelihood estimation (MLE). The main objective of this chapter is to identify the best approach between Bayesian and frequentist one for at-site FFA. As a case study, the data of only two stations were used, Kotri and Rasul, and Bayesian and MLE approaches were implemented. Most commonly used tests were applied for checking initial assumptions. Goodness of fit (GOF) tests were used to identify the best model, which indicated that the generalized extreme value (GEV) distribution appeared to be best fitted for both stations. Under Bayesian paradigm, quantile estimates are constructed using Markov Chain Monte Carlo (MCMC) simulation method for their respective returned periods and non-exceedance probabilities. For MCMC simulations, as compared to other sampler, the M-H sampling technique was used to generate a large number of parameters. The analysis indicated that the standard errors of the parameters' estimates and ultimately the quantiles' estimates using Bayesian methods remained less as compared to maximum likelihood estimation (MLE), which shows the superiority of Bayesian methods over conventional ones in this study. Further, the safety amendments under two techniques were also calculated, which also show the robustness of Bayesian method over MLE. The outcomes of these analyses can be used in the selection of better design criteria for water resources management, particularly in flood mitigation.

Related Content

Nalluri Poojitha, B. Ramya Kuber, Ambati Vanshika. © 2024. 24 pages.
Mandeep Kaur, Rajni Aron, Heena Wadhwa, Righa Tandon, Htet Ne Oo, Ramandeep Sandhu. © 2024. 26 pages.
Richa Saxena, Vaishnavi Srivastava, Dipti Bharti, Rahul Singh, Amit Kumar, Abhilekha Sharma. © 2024. 20 pages.
Inzimam Ul Hassan, Zeeshan Ahmad Lone, Swati Swati, Aya Gamal. © 2024. 23 pages.
Rakhi Chauhan, Neera Batra, Sonali Goyal, Amandeep Kaur. © 2024. 16 pages.
Risha Dhargalkar, Viosha Cruz, Abdullah Alzahrani. © 2024. 17 pages.
Dipankar Ghosh, Sayan Adhikary, Srijaa Sau. © 2024. 25 pages.
Body Bottom