scholarly journals Reverse Flood Routing in an Open Channel Using Genetic Algorithm

2021 ◽  
Author(s):  
Ali Azizipour ◽  
Seyed Mahmood Kashefipour ◽  
Ali Haghighi
Keyword(s):  
Genetic Algorithm ◽  
Open Channel ◽  
Fitness Function ◽  
Flood Routing ◽  
Integrated Method ◽  
Karun River ◽  
The Status ◽  
Genetic Algorithm Method ◽  
Routing Method ◽  
Hydrometric Station

Flood routing in flood forecasting issue, calculation the height of flood bands, determining the river boundaries, and estimation of protective facilities for flood –exposed building is applicable. In many cases, due to the lack of measuring stations, the status of the upstream flood generating hydrograph is not known. The purpose of this study is to present an integrated method comprising of an optimization model and a hydrodynamic numerical model for flood modeling to determine the upstream hydrograph using the provided hydrograph at the downstream measuring station of a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph which is generated in the first step; (3) compare the calculated and observed hydrograph in downstream by using a fitness function. This recommended procedure was named as Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparing the generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at Ahvaz hydrometric station, as an ungauged river location, shows the high accuracy of the recommended model in this study.

scholarly journals Reverse flood routing in an open channel using a combined model of genetic algorithm and a numerical model

2021 ◽  
Vol 16 (4) ◽  
pp. 1465-1474
Author(s):  
Ali Azizipour ◽  
Seyed Mahmood Kashefipour ◽  
Ali Haghighi
Keyword(s):  
Genetic Algorithm ◽  
Numerical Model ◽  
Rural Areas ◽  
Fitness Function ◽  
River System ◽  
Flood Routing ◽  
Integrated Method ◽  
Karun River ◽  
The Status ◽  
Urban And Rural Areas

Abstract Flood impact assessment in a river system is done with the help of flood routing and this process helps to determine the status of sensitive points of the route in terms of flood entry and the resulting risks for urban and rural areas. For flood routing, a hydrodynamic numerical model should be implemented and this model needs upstream and downstream boundaries. In some cases, the upstream boundary, which is usually a hydrograph, is not available due to the lack of facilities and it is necessary to be generated for numerical model implementation. The purpose of this study is to present an integrated method comprising an optimization model and a hydrodynamic numerical model for flood modeling in order to determine the upstream hydrograph using the measured downstream hydrograph along a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using the genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph, which is generated in the first step; (3) comparing the calculated and observed hydrograph in the downstream by using a fitness function. This recommended procedure was named the Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparison of the final generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at the upstream boundary (here Ahvaz hydrometric station was assumed as an ungauged river location) shows the high accuracy of the recommended model in this study.

Energy-Balanced Routing Method for Wireless Cooperative Wireless Network

2013 ◽  
Vol 774-776 ◽  
pp. 1659-1663
Author(s):  
Yan Xin Yao ◽  
Qiu Shi Liu
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Wireless Network ◽  
Fitness Function ◽  
Path Loss ◽  
New Method ◽  
Transmission Path ◽  
Routing Method ◽  
Energy Balanced Routing

This paper presents a new method for optimizing energy consumption of wireless network. This new method tries to keep the energy consumption of the whole network while balancing the energy consumption of each node. In particular, we focus on the routing method to shorten the transmission path for reducing the energy path loss. We perform this by introducing an appropriate fitness function with the Genetic Algorithm. This fitness function is designed in a dedicate way so that the energy consumption minimization and energy consumption balance between nodes could be fulfilled simultaneously. Simulations validate that the proposed method could keep energy consumption and balance the energy consumption simultaneously to a better extent.

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.

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 Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Hybrid Optimization Based Mathematical Procedure for Dimensional Synthesis of Slider-Crank Linkage

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1581
Author(s):  
Alfonso Hernández ◽  
Aitor Muñoyerro ◽  
Mónica Urízar ◽  
Enrique Amezua
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Linear Equations ◽  
Optimization Procedure ◽  
Optimization Techniques ◽  
Dimensional Synthesis ◽  
Hybrid Strategy ◽  
Dimensional Parameters ◽  
Optimization Methodology ◽  
Crank Mechanism

In this paper, an optimization procedure for path generation synthesis of the slider-crank mechanism will be presented. The proposed approach is based on a hybrid strategy, mixing local and global optimization techniques. Regarding the local optimization scheme, based on the null gradient condition, a novel methodology to solve the resulting non-linear equations is developed. The solving procedure consists of decoupling two subsystems of equations which can be solved separately and following an iterative process. In relation to the global technique, a multi-start method based on a genetic algorithm is implemented. The fitness function incorporated in the genetic algorithm will take as arguments the set of dimensional parameters of the slider-crank mechanism. Several illustrative examples will prove the validity of the proposed optimization methodology, in some cases achieving an even better result compared to mechanisms with a higher number of dimensional parameters, such as the four-bar mechanism or the Watt’s mechanism.

Research and Application on Technology of Flood-Storage and Flood-Detention of Coal Mining Subsidence Area of Suzhou Coal Mine

2014 ◽  
Vol 641-642 ◽  
pp. 80-83
Author(s):  
Jia Zhong Zheng ◽  
Mei Zhu ◽  
Zheng Long Wang
Keyword(s):  
Coal Mining ◽  
Dynamic Change ◽  
Storage Conditions ◽  
Flood Routing ◽  
Mining Subsidence ◽  
Subsidence Area ◽  
The Status ◽  
Suzhou City ◽  
Change Trend ◽  
Coal Mining Subsidence

The artical is based on the investigation of the basis of the status quo of Zhuxianzhuang and Luling coal mining subsidence area in Anhui province Suzhou city(hereinafter referred to as the "Zhu Lu subsidence area"), a preliminary analysis of the dynamic change trend of detention space in Zhu Lu subsidence area, and based on the hysteresis storage conditions of subsidence area, use the flood routing model to simulate the hysteresis effect of storage at different subsidence scenarios of different frequency flood. Finally, using the experience type channel evolution model and peak delay routing model further revealed storage effect on flood process of Zhu Lu subsidence area.

scholarly journals The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020 ◽  
Vol 12 (23) ◽  
pp. 9818
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Nikoleta Mikušová
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Problem ◽  
Software Tool ◽  
Time Saving ◽  
General Validity ◽  
Resistance Function ◽  
Genetic Algorithm Method ◽  
Working Day ◽  
Enterprise Logistics

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

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