scholarly journals Study on Optimal Allocation of Water Resources Based on Surrogate Model of Groundwater Numerical Simulation

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 831
Author(s):  
Wang ◽  
Cui ◽  
Shao ◽  
Zhang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Water Resources ◽  
Simulation Model ◽  
Bp Neural Network ◽  
Surrogate Model ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Water Shortage ◽  
Simulation And Optimization

The characteristics of groundwater systems are highly complex. It will take substantial computational resources and running time to optimize a groundwater numerical simulation model. In this study, in order to realize the coupling of simulation and optimization models, the improved backpropagation (BP) neural network was used as a surrogate model of a groundwater numerical simulation; the improved BP neural network was trained with the groundwater level drawdown–pumping volume data output of the simulation model. The method was applied to the water resource optimal allocation in the near future of Wenshang County, Shandong Provence of China. The results show that the water level drawdown output of the improved BP neural network model fits the results of the simulation model well, showing that the improved BP neural network can effectively be the surrogate of a groundwater numerical simulation to be embedded in an optimization model. The improved simulation and optimization technique can make full use of water resources in the whole area. Under an assurance rate of 50%, both water shortage and water shortage rate reduced to zero in the whole area. Under an assurance rate of 75%, water shortage and water shortage rate reduced to about 10% of the conventional scheme, which dramatically improves the comprehensive benefit of the whole area.

scholarly journals Assessment of water resource security in karst area of Guizhou Province, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liying Liu
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Water Resource ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Water Shortage ◽  
Guizhou Province ◽  
Karst Area ◽  
Water Resource Security ◽  
Resource Security

AbstractThis paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning.

scholarly journals Macro Modeling of V-Shaped Electro-Thermal MEMS Actuator with Human Error Factor

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 622
Author(s):  
Dongpeng Zhang ◽  
Anjiang Cai ◽  
Yulong Zhao ◽  
Tengjiang Hu
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Bp Neural Network ◽  
Human Error ◽  
Calculation Model ◽  
Fuzzy Mathematics ◽  
Ansys Simulation ◽  
Macro Modeling ◽  
Error Factor ◽  
Simulation Results

The V-shaped electro-thermal MEMS actuator model, with the human error factor taken into account, is presented in this paper through the cascading ANSYS simulation model and the Fuzzy mathematics calculation model. The Fuzzy mathematics calculation model introduces the human error factor into the MEMS actuator model by using the BP neural network, which effectively reduces the error between ANSYS simulation results and experimental results to less than 1%. Meanwhile, the V-shaped electro-thermal MEMS actuator model, with the human error factor included, will become more accurate as the database of the V-shaped electro-thermal actuator model grows.

Thermal–Mechanical Coupling Evaluation of the Panel Performance of a Prefabricated Cabin-Type Substation Based on Machine Learning

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 93
Author(s):  
Xiangsheng Lei ◽  
Jinwu Ouyang ◽  
Yanfeng Wang ◽  
Xinghua Wang ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Numerical Simulation ◽  
Fire Resistance ◽  
Bp Neural Network ◽  
Stress Test ◽  
Normal Operation ◽  
Model Parameters ◽  
Mechanical Coupling ◽  
The Impact

The panel performance of a prefabricated cabin-type substation under the impact of fires plays a vital role in the normal operation of the substation. However, current evaluations of the panel performance of substations under fire still focus on fire resistance tests, which seldom consider the relationship between fire behavior and the mechanical load of the panel under the impact of fires. Aiming at the complex and uncertain relationship between the thermal and mechanical performance of the substation panel under impact of fires, this paper proposes a machine learning method based on a BP neural network. First, the fire resistance test and the stress test of the panel is carried out, then a machine learning model is established based on the BP neural network. According to the collected data, the model parameters are obtained through a series of training and verification processes. Meanwhile, the correlation between the panel performance and fire resistance was obtained. Finally, related parameters are input into the thermal–mechanical coupling evaluation model for the substation panel performance to evaluate the fire resistance performance of the substation panel. To verify the correctness of the established model, numerical simulation of the fire test and stress test of the panel is conducted, and numerical simulation samples are predicted by the trained model. The results show that the prediction curve of neural network is closer to the real results compared with the numerical simulation, and the established model can accurately evaluate the thermal–mechanical coupling performance of the substation panel under fire.

scholarly journals Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 36 ◽  
Author(s):  
Jian Lv ◽  
Miaomiao Zhu ◽  
Weijie Pan ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Surrogate Model ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Interactive Evolutionary Computation ◽  
Interactive Genetic Algorithm ◽  
Novel Design ◽  
Fuzzy Interval

To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns.

A New Approach to Analyzing Gas Mixtures Based on Improved BP Neural Network Algorithm

2012 ◽  
Vol 260-261 ◽  
pp. 548-553
Author(s):  
Teng Li ◽  
Xiao Mei Yuan ◽  
Shi Liang Yang ◽  
Xin Hui Zhang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Bp Neural Network ◽  
Array Signal Processing ◽  
Sensor Arrays ◽  
Gas Mixtures ◽  
New Approach ◽  
Gas Concentration ◽  
Network Algorithm ◽  
Neural Network Algorithm

A new approach is presented for analyzing gas mixtures by transforming the problem into a pattern classification one to reduce the effect of the poor repeatability of sensor response on the prediction of gas concentration. The aim of numerical simulation is to determine how successfully the approach using the combination of artificial neural networks with multi-sensor arrays can analyze multi-component gas mixtures. The results indicate that the new approach is realistic for gas mixture analysis, and numerical simulation is a powerful tool to determine the architecture of a network. By constructing improved BP neural network algorithm and basic BP neural network into sensor array signal processing and extracting 6 component as the input of neural network, Our investigation results indicated that recognition results obtained from improved BP neural network algorithm more accuracy than the results obtained from basic BP neural network.

Research on the Feasibility of Water Diversion Project

2014 ◽  
Vol 580-583 ◽  
pp. 1874-1877
Author(s):  
Li Hua Zhang ◽  
Guang Hui Wang ◽  
Xiao Hui Hao
Keyword(s):  
Water Resources ◽  
Optimal Allocation ◽  
Water Shortage ◽  
The Other ◽  
Water Diversion ◽  
Actual Condition ◽  
Other Hand ◽  
Water Diversion Project ◽  
Engineering Measures ◽  
The One

The research on the feasibility of Feicheng Water Diversion from Dawen River Project is based on a thorough investigation in the actual condition of water resources in Feicheng city. It is found that on the one hand this city has been suffering from water shortage, while on the other hand it has allowed most of the water flowing away in vain from Dawen River, which runs through this area. To resolve this contradiction, this research demonstrates the feasibility of networking of Dawen River and Shangzhuanglu Reservoir through engineering measures to realize the optimal allocation of water resources

scholarly journals Study on optimal allocation of water resources in Dujiangyan irrigation district of China based on improved genetic algorithm

2020 ◽  
Author(s):  
Ruihuan Li ◽  
Yingli Chang ◽  
Zhaocai Wang
Keyword(s):  
Genetic Algorithm ◽  
Water Resources ◽  
Optimal Allocation ◽  
Water Shortage ◽  
Vertical Dimension ◽  
Allocation Model ◽  
Genetic Operator ◽  
Irrigation Area ◽  
Use Of Resources ◽  
Constraint Model

Abstract In order to distribute water resources reasonably, it is convenient to make full use of resources and produce high economic and social benefits. Taking the Dujiangyan irrigation area of China as an example, we discuss the idea of establishing and solving the optimal allocation model of water resources. Aiming at this area, a two-dimensional constraint model with the highest economic value, the minimum water shortage, the minimum underground water consumption and the necessary living water demand is established. In order to solve this model, we improve the multi-population genetic algorithm, extend the genetic optimization of the algorithm into two dimensions, take the population as the vertical dimension and the individual as the horizontal dimension, and transforms the cross genetic operator to copy the genetic operator and the mutation operator to only act on the vertical dimension, so as to optimize the allocation of such discrete objectives of water resources in the irrigation area with the particular model suitable for the region. The distribution results successfully control the water shortage rate of each area at a low level, which save the exploitation of groundwater to the maximum extent and produce high economic benefits. The improved algorithm proposed in this paper has a kind of strong optimization ability and provides a new solution for the optimization problem with multiple constraints.

scholarly journals Application of Whale Optimization Algorithm in Optimal Allocation of Water Resources

2018 ◽  
Vol 53 ◽  
pp. 04019 ◽  
Author(s):  
Zhihong Yan ◽  
Shuqian Wang ◽  
Bin Liu ◽  
Xinde Li
Keyword(s):  
Water Resources ◽  
Optimization Algorithm ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Water Shortage ◽  
Whale Optimization Algorithm ◽  
Total Water ◽  
Water Users ◽  
Whale Optimization ◽  
Development And Utilization

In order to solve the water crisis, it is important to optimize the allocation of water resources. In this paper, the Whale Optimization Algorithm (WOA) is applied to the optimal allocation of water resources in Xingtai with the goal of maximum economic benefit and minimum total water shortage. The results show that the total water demand of different water users in each district is 26.94×108m3, the total allocated water is 19.83×108m3, the total water shortage is 7.11×108m3, and the water shortage rate was 26.39%. The lack of water is mainly concentrated in the primary industry. The result of the solution reflects the principle of water supply order and water use equity, which is in line with the actual development and utilization of water resources in the study area. It also verifies the feasibility of the whale optimization algorithm, such as less parameter adjustment, faster convergence, and better global optimization ability when solving water resources optimization problems.

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