Saba Mirza Alipour

Saba Mirza Alipour
Universitetet i Agder | UIA · Renewable energy

PhD student

About

6
Publications
1,111
Reads
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11
Citations
Introduction
River flood prediction is a challenging task due to complex, dynamic and stochastic nature of the river systems. Aggregating the affecting parameters and interactions between them into a forecasting model brings uncertainties to all components of the flood forecasting (e.g., inputs, model structure). We aim at: 1) the development and complete implementation of a high-performance mathematical model for flood propagation, based on emerging technologies such as parallel, distributed and graphics-based computing and 2) explore the impact of various sources of uncertainty on the results of two-dimensional hydraulic modeling.
Additional affiliations
November 2015 - present
West Azerbaijan Regional Water Company
Position
  • Engineer

Publications

Publications (6)
Article
Full-text available
Uncertainties associated with the estimation of design rainfall is one of the major sources of uncertainty in flood modeling. Representation of these uncertainties can be challenging due to the incomplete historical data, climate change effect and the stochastic nature of the weather. In this study, a Bayesian inference with prior knowledge about t...
Article
Full-text available
Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results; however, they are computationally costly and therefore unsuitable for many real time applications and uncertainty analysis that requires a large number of model realizations. Theref...
Article
Full-text available
Sensitivity analysis is a commonly used technique in hydrological modeling for different purposes, including identifying the influential parameters and ranking them. This paper proposes a simplified sensitivity analysis approach by applying the Taguchi design and the ANOVA technique to 2D hydrodynamic flood simulations, which are computationally in...
Conference Paper
Full-text available
River flood return period estimation plays an important role in the engineering practices of water resources and flood management, but there are many parameters that accompany the calculation process. Typically, several return levels and return periods (e.g. 100, 200, etc.) are used to describe and quantify river flood discharge. Classical river fl...
Article
Full-text available
In modeling of overland flow and erosion, the overland flow friction factor (f), is a crucial factor. Due to the importance of a good understanding of f and its variability, the current study aimed to investigate the capability of non-linear approaches to estimate the Darcy-Weisbach friction factor of overland flow and its components (sediment tran...
Article
Due to the significance of overland flow resistance (f) in hillslope hydrology and models of erosion, the present study peruses the capability of non-linear approaches to estimate the overland flow resistance and its components. For this purpose, numerous support vector machine (SVM) models were developed and tested using four series of flume exper...

Questions

Questions (3)
Question
I have a data set consists of 1000 samples with an almost normal distribution (skewed to the left). Can I use clustering methods to find a minimum sample (e.g., 50 samples) that represents the original sample's behavior? If yes, which clustering method can be useful?
Thank you in advance for the help! :)
Question
Attached you will find the "plot(gev0,type = "qq2") " command's results after each time ruining.
Question
I am looking for maps that provide information such as: soil type, degree of saturation, and any other parameter that can be used to estimate infiltration rate.

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Projects

Project (1)
Project
There are many different sources of uncertainty in flood modelling, and their impact may be to change the way in which models are evaluated and decisions are made. This project aims at providing better insight into the uncertainty of flood water levels due to the uncertain parameters of the floodplain.