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 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.

Parameter Identification of Nonlinear Muskingum Model with Backtracking Search Algorithm

2016 ◽  
Vol 30 (8) ◽  
pp. 2767-2783 ◽  
Author(s):  
Xiaohui Yuan ◽  
Xiaotao Wu ◽  
Hao Tian ◽  
Yanbin Yuan ◽  
Rana Muhammad Adnan
Keyword(s):  
Parameter Identification ◽  
Search Algorithm ◽  
Backtracking Search ◽  
Backtracking Search Algorithm ◽  
Muskingum Model ◽  
Nonlinear Muskingum Model

scholarly journals Multi-Objective Parameter Estimation of Improved Muskingum Model by Wolf Pack Algorithm and Its Application in Upper Hanjiang River, China

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1415 ◽  
Author(s):  
Tao Bai ◽  
Jian Wei ◽  
Wangwang Yang ◽  
Qiang Huang
Keyword(s):  
Parameter Estimation ◽  
Flood Routing ◽  
Practical Significance ◽  
Absolute Difference ◽  
Model Parameters ◽  
Multi Objective ◽  
Muskingum Model ◽  
Efficient Alternative ◽  
Hanjiang River ◽  
Swarm Intelligence Optimization

In order to overcome the problems in the parameter estimation of the Muskingum model, this paper introduces a new swarm intelligence optimization algorithm—Wolf Pack Algorithm (WPA). A new multi-objective function is designed by considering the weighted sum of absolute difference (SAD) and determination coefficient of the flood process. The WPA, its solving steps of calibration, and the model parameters are designed emphatically based on the basic principle of the algorithm. The performance of this algorithm is compared to the Trial Algorithm (TA) and Particle Swarm Optimization (PSO). Results of the application of these approaches with actual data from the downstream of Ankang River in Hanjiang River indicate that the WPA has a higher precision than other techniques and, thus, the WPA is an efficient alternative technique to estimate the parameters of the Muskingum model. The research results provide a new method for the parameter estimation of the Muskingum model, which is of great practical significance to improving the accuracy of river channel flood routing.

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

Inspirations from Nature for Meta-heuristic Algorithms: A Survey

2019 ◽  
Vol 13 ◽  
Author(s):  
Rohit Kumar Sachan ◽  
Dharmender Singh Kushwaha
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Water Cycle ◽  
Search Algorithm ◽  
Theoretical Perspective ◽  
Gravitational Search Algorithm ◽  
Natural Phenomenon ◽  
Flower Pollination Algorithm ◽  
Shuffled Frog Leaping ◽  
Nature Inspired Algorithms

Background: Nature-Inspired Algorithms (NIAs) are the most efficient way to solve advanced engineering and real-world optimization problems. Since the last few decades, various researchers have proposed an immense number of NIAs. These NIAs get inspiration from natural phenomenon. A young researcher attempting to undertake or solve a problem using NIAs is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some scores over others. Objective: This paper presents a comprehensive study of seven NIAs, which have new and unique inspirations. This study shall useful to easily understand the fundamentals of NIAs for any new entrant. Conclusion: Here, we classify the NIAs as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, well-establish and relatively new NIAs, namely- Shuffled Frog Leaping Algorithm (SFLA), Firefly Algorithm (FA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), Water Cycle Algorithm (WCA), Jaya Algorithm and Anti-Predatory NIA (APNIA), have been studied. This study presents a theoretical perspective of NIAs in a simplified form based on its source of inspiration, mathematical formulations, control parameters, features, variants and area of application, where these algorithms have been successfully applied.

A Comparative Analysis on Economic Load Dispatch Problem Using Soft Computing Techniques

2020 ◽  
Vol 12 (2) ◽  
pp. 50-73 ◽  
Author(s):  
O.V. Singh ◽  
M. Singh
Keyword(s):  
Search Algorithm ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Optimization Techniques ◽  
Flower Pollination Algorithm ◽  
Economic Load Dispatch ◽  
Flower Pollination ◽  
Soft Computing Techniques ◽  
Equality And Inequality Constraints ◽  
Optimization Search

This article aims at solving economic load dispatch (ELD) problem using two algorithms. Here in this article, an implementation of Flower Pollination (FP) and the BAT Algorithm (BA) based optimization search algorithm method is applied. More than one objective is hoped to be achieve in this article. The combined economic emission dispatch (CEED) problem which considers environmental impacts as well as the cost is also solved using the two algorithms. Practical problems in economic dispatch (ED) include both nonsmooth cost functions having equality and inequality constraints which make it difficult to find the global optimal solution using any mathematical optimization. In this article, the ELD problem is expressed as a nonlinear constrained optimization problem which includes equality and inequality constraints. The attainability of the discussed methods is shown for four different systems with emission and without emission and the results achieved with FP and BAT algorithms are matched with other optimization techniques. The experimental results show that conferred Flower Pollination Algorithm (FPA) outlasts other techniques in finding better solutions proficiently in ELD problems.

Flower Pollination Algorithm for Software Effort Coefficients Optimization to Improve Effort Estimation Accuracy

2021 ◽  
Vol 9 (2) ◽  
pp. 139
Author(s):  
Alifia Puspaningrum ◽  
Fachrul Pralienka Bani Muhammad ◽  
Esti Mulyani
Keyword(s):  
Search Algorithm ◽  
Cost Model ◽  
Cuckoo Search ◽  
Estimation Accuracy ◽  
Flower Pollination Algorithm ◽  
Metaheuristic Algorithm ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Flower Pollination ◽  
Cocomo Ii

Software effort estimation is one of important area in project management which used to predict effort for each person to develop an application. Besides, Constructive Cost Model (COCOMO) II is a common model used to estimate effort estimation. There are two coefficients in estimating effort of COCOMO II which highly affect the estimation accuracy. Several methods have been conducted to estimate those coefficients which can predict a closer value between actual effort and predicted value.  In this paper, a new metaheuristic algorithm which is known as Flower Pollination Algorithm (FPA) is proposed in several scenario of iteration. Besides, FPA is also compared to several metaheuristic algorithm, namely Cuckoo Search Algorithm and Particle Swarm Optimization. After evaluated by using Mean Magnitude of Relative Error (MMRE), experimental results show that FPA obtains the best result in estimating effort compared to other algorithms by reached 52.48% of MMRE in 500 iterations.

Sign in / Sign up

Export Citation Format

Share Document