scholarly journals A new method for dividing flood period in the variable-parameter Muskingum models

2021 ◽  
Author(s):  
Reyhaneh Akbari ◽  
Masoud-Reza Hessami-Kermani
Keyword(s):  
Objective Function ◽  
Variable Parameter ◽  
Flood Routing ◽  
Muskingum Model ◽  
Proposed Model ◽  
Cut Method ◽  
Nonlinear Muskingum Model ◽  
Muskingum Routing ◽  
Flood Period ◽  
Do So

Abstract The Muskingum routing model is favored by water engineers owing to its simplicity and accuracy. A large amount of research is done to improve the accuracy of the model. One way to do so is to consider variable hydrological parameters during the flood routing period. In this study, the random selection (RS) method was proposed to divide the flood period of the nonlinear Muskingum model into three sub-periods. The proposed method was based on RS of members in each sub-region. It was applied to rout three flood hydrographs, and the objective function was the sum of squared errors. Comparing the results from the three variable-parameter nonlinear Muskingum model with those from the variable-parameter nonlinear Muskingum models in previous studies, the proposed model optimized the objective function in these hydrographs up to 61%. The uncertainty analysis of Muskingum parameters for Wilson's hydrograph was performed by the fuzzy alpha cut method, and it was found that the uncertainty of the parameter x is greater than the uncertainty of the parameters k and m.

scholarly journals Optimization of the Nonlinear Muskingum Model Parameters for the River Routing, Tigris River a Case Study

2021 ◽  
Vol 16 (6) ◽  
pp. 649-656
Author(s):  
Maher Abd Ameer Kadim ◽  
Isam Issa Omran ◽  
Alaa Ali Salman Al-Taai
Keyword(s):  
Genetic Algorithm ◽  
Least Squares Method ◽  
Harmony Search ◽  
Flood Routing ◽  
Model Parameters ◽  
Muskingum Model ◽  
Tigris River ◽  
Nonlinear Muskingum Model ◽  
Genetic Algorithm Method

Flood forecasting and management are one of the most important strategies necessary for water resource and decision planners in combating flood problems. The Muskingum model is one of the most popular and widely used applications for the purpose of predicting flood routing. The particle swarm optimization (PSO) methodology was used to estimate the coefficients of the nonlinear Muskingum model in this study, comparing the results with the methods of genetic algorithm (GA), harmony search (HS), least-squares method (LSM), and Hook-Jeeves (HJ). The average monthly inflow for the Tigris River upstream at the Al-Mosul dam was selected as a case study for estimating the Muskingum model's parameters. The analytical and statistical results showed that the PSO method is the best application and corresponds to the results of the Muskingum model, followed by the genetic algorithm method, according to the following general descending sequence: PSO, GA, LSM, HJ, HS. The PSO method is characterized by its accurate results and does not require many assumptions and conditions for its application, which facilitates its use a lot in the subject of hydrology. Therefore, it is better to recommend further research in the use of this method in the implementation of future studies and applications.

A Re-Parameterized and Improved Nonlinear Muskingum Model for Flood Routing

2015 ◽  
Vol 29 (9) ◽  
pp. 3419-3440 ◽  
Author(s):  
Omid Bozorg Haddad ◽  
Farzan Hamedi ◽  
Hosein Orouji ◽  
Maryam Pazoki ◽  
Hugo A. Loáiciga
Keyword(s):  
Flood Routing ◽  
Muskingum Model ◽  
Nonlinear Muskingum Model

General Review of Calibration Process of Nonlinear Muskingum Model and Its Optimization by Up-to-Date Methods

2020 ◽  
pp. 61-83
Author(s):  
Umut Kırdemir ◽  
Umut Okkan
Keyword(s):  
Water Cycle ◽  
Search Algorithm ◽  
Flood Routing ◽  
Flower Pollination Algorithm ◽  
Model Parameters ◽  
Flower Pollination ◽  
Muskingum Model ◽  
Colliding Bodies Optimization ◽  
Nonlinear Muskingum Model ◽  
Outflow Hydrograph

Nonlinear Muskingum method is a very efficient tool in flood routing implementation. It is possible to estimate an outflow hydrograph by a given inflow hydrograph of a flood at a specific point of the river channel. However, it turns out an optimization problem at the stage of employing this method, and it becomes important to reach the optimal model parameters so as to obtain precise outflow hydrograph estimations. Hence, it was decided to utilize five up-to-date optimization algorithms, namely, vortex search algorithm (VSA), gases brownian motion algorithm (GBMO), water cycle algorithm (WCA), flower pollination algorithm (FPA), and colliding bodies optimization (CBO). The algorithms were integrated with the nonlinear Muskingum model so as to estimate the outflow hydrograph of Wilson data, and it was deduced that WCA, FPA, and VSA perform relatively better than the models employed in the other researches before.

scholarly journals Developing a Novel Parameter-free Optimization Algorithm for Flood Routing

2021 ◽  
Author(s):  
Omid Bozorg-Haddad ◽  
Parisa Sarzaeim ◽  
Hugo A. Loáiciga
Keyword(s):  
Optimization Algorithm ◽  
Flood Routing ◽  
Benchmark Problems ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Metaheuristic Optimization ◽  
Muskingum Model ◽  
Predictive Skill ◽  
Muskingum Routing ◽  
Teaching Learning

Abstract The Muskingum model is a popular hydrologic flood routing method; however, the accurate estimation of Muskingum model parameters is a critical task in the successful and precise implementation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation associated with various complex models including the nonlinear Muskingum model. Among EMOAs, teaching-learning-based optimization (TLBO) is a relatively new parameterless metaheuristic optimization algorithm, inspired by the relationship between teacher and students in a classroom to improve the overall knowledge of a topic in a class. This paper presents an application of TLBO to estimate Muskingum model parameters by minimizing the prediction error of outflow measurements. Several examples evaluate and confirm the successful performance of TLBO for the estimation of Muskingum-routing parameters precisely. The results show TLBO-Muskingum’s high accuracy for estimating accurately Muskingum’s parameters based on the Nash-Sutcliffe Efficiency (NSE) to evaluate the TLBO’s predictive skill with benchmark problems.

Nonlinear Muskingum Model for Flood Routing in Irrigation Canals Using Storage Moving Average

2016 ◽  
Vol 142 (5) ◽  
pp. 04016010 ◽  
Author(s):  
Farzan Hamedi ◽  
Omid Bozorg-Haddad ◽  
Hossein Orouji ◽  
Elahe Fallah-Mehdipour ◽  
Hugo A. Loáiciga
Keyword(s):  
Moving Average ◽  
Flood Routing ◽  
Irrigation Canals ◽  
Muskingum Model ◽  
Nonlinear Muskingum Model

Parameter estimation of an improved nonlinear Muskingum model using a new hybrid method

2016 ◽  
Vol 48 (5) ◽  
pp. 1253-1267 ◽  
Author(s):  
Majid Niazkar ◽  
Seied Hosein Afzali
Keyword(s):  
Parameter Estimation ◽  
Evaluation Criteria ◽  
Variable Parameter ◽  
Optimization Techniques ◽  
Flood Routing ◽  
Gradient Algorithm ◽  
Hybrid Technique ◽  
Muskingum Model ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

Although various techniques have been proposed to estimate the parameters of different versions of the Muskingum model, more rigorous techniques and models are still required to improve the computational precision of the calibration process. In this research, a new hybrid technique was proposed for Muskingum parameter estimation. Based on the conducted comprehensive literature review on the Muskingum flood routing models, a new improved Muskingum model with nine constant parameters was presented. Since the inflow-weighted parameter in the proposed model is a function of inflow hydrograph, it varies during the flood period and consequently can also be considered as a variable-parameter Muskingum model. The new hybrid technique was successfully applied for parameter estimation of the new version of Muskingum model for two case studies selected from the literature. Results were compared with those of other methods using several common performance evaluation criteria. The new Muskingum model significantly reduces the sum of the square of the deviations between the observed and routed outflows (SSQ) value for the double-peak case study. Finally, the obtained results indicate that not only the hybrid modified honey bee mating optimization-generalized reduced gradient algorithm somehow overcomes the shortcomings of both zero and first-order optimization techniques, but also the new Muskingum model appears to be the most reliable Muskingum version compared with the other methods considered in this study.

scholarly journals Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization

2021 ◽  
Vol 13 (13) ◽  
pp. 7152
Author(s):  
Mike Spiliotis ◽  
Alvaro Sordo-Ward ◽  
Luis Garrote
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Flood Routing ◽  
Performance Criteria ◽  
Extension Principle ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Muskingum Method ◽  
Fuzzy Parameters

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions.

scholarly journals Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3896
Author(s):  
Dat Ngo ◽  
Gi-Dong Lee ◽  
Bongsoon Kang
Keyword(s):  
Objective Function ◽  
Density Estimation ◽  
Digital Terrain Model ◽  
Single Image ◽  
Image Dehazing ◽  
Terrain Model ◽  
Digital Terrain ◽  
Proposed Model ◽  
Synthetic Datasets ◽  
Computation Analysis

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.

Mathematical Modeling and Analysis of Covid-19 Infection spreads in India with Restricted Optimal Treatment on Disease Incidence

BIOMATH ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 2106147
Author(s):  
Debkumar Pal ◽  
D Ghosh ◽  
P K Santra ◽  
G S Mahapatra
Keyword(s):  
Mathematical Modeling ◽  
Objective Function ◽  
Reproduction Number ◽  
Disease Incidence ◽  
Treatment Control ◽  
Modeling And Analysis ◽  
Proposed Model ◽  
The Cost ◽  
Disease Free ◽  
The Basic Reproduction Number

This paper presents the current situation and how to minimize its effect in India through a mathematical model of infectious Coronavirus disease (COVID-19). This model consists of six compartments to population classes consisting of susceptible, exposed, home quarantined, government quarantined, infected individuals in treatment, and recovered class. The basic reproduction number is calculated, and the stabilities of the proposed model at the disease-free equilibrium and endemic equilibrium are observed. The next crucial treatment control of the Covid-19 epidemic model is presented in India's situation. An objective function is considered by incorporating the optimal infected individuals and the cost of necessary treatment. Finally, optimal control is achieved that minimizes our anticipated objective function. Numerical observations are presented utilizing MATLAB software to demonstrate the consistency of present-day representation from a realistic standpoint.

Sign in / Sign up

Export Citation Format

Share Document