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Research on Artificial Neural Network and Genetic Algorithm for the Optimizing of Schemes of the Water Pollution Control Planning

Author: LiShengHai
Tutor: GuoJinSong
School: Chongqing University
Course: Municipal Engineering
Keywords: Artificial Neural Networks Genetic Algorithms Water Pollution Control Optimization
CLC: X52
Type: Master's thesis
Year: 2002
Downloads: 414
Quote: 3
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Abstract


Artificial Neural Network (ANN) and genetic algorithm (Genetic Algorithms abbreviated as GA) is the forefront of complex nonlinear science and artificial intelligence science, and its application in the field of water pollution control research is still in its infancy in China and abroad. In this paper, a more comprehensive analysis reviewed the research status of the water pollution control methods, and a brief introduction to the basic principles of the ANN and GA optimization algorithm based on the first time in the country, the ANN method discrete Hopfield network (Discrete Hopfield Neural Networks shorthand for DHNN ) and GA introduce the field of water pollution control planning, some exploratory research, mainly in the optimization of water pollution control programs and efforts to improve the level of water pollution control program optimization. According to the Water Pollution Control Features and Hopfield network energy function under certain conditions converges to the characteristics of the lowest point of a certain region, through the rational design of DHNN energy function, the cost of water pollution control program into DHNN the energy function, even if The lowest point of the water pollution control program costs the the lowest program with DHNN of energy function corresponding to water pollution control the optimization DHNN model and mathematically rigorous and Proof of convergence the DHNN energy function. The case study shows that: DHNN optimal solution takes full advantage of the environmental capacity of the natural water body, and, in the process of selection optimization program, when the cross-section of a control certain pollutants in excess of prescribed water quality standards, DHNN First can reduce concentration of the portfolio of the lowest cost in the control section of a pollutant contribution units, and thus can quickly obtain the optimal portfolio. GA is a universal numerical method, and almost no requirements on the nature of the objective function, does not even require explicitly write the objective function. GA is used to optimize the calculation can not deal directly with the parameters of the problem space, and can only deal with individual gene chain code. This article subtly Genetic Algorithm gene chain code with the water pollution control program combined, that the form of real-coded to represent the various sources of pollution does not deal with real numbers 1,2,3 to, primary treatment, secondary treatment, At the same time, corresponding to the total cost of the individual fitness function with portfolio portfolio the total cost of the smallest individual fitness function value so excellent individuals by GA correspond to the optimal or approximate the optimal solution . The case study shows that: in less than a GA is randomly selected from the initial value, and surviving in the algorithm, leading to the the GA results obtained in the optimal solution to oscillate around. However, due to the always retain a certain number of fine individual GA In operation, therefore, these results should be a set of optimal or approximation of the optimal results, i.e. the the GA results obtained are to converge to the optimal solution in the neighborhood of Decision-makers through feasibility, economic, and scientific comparison to find the optimal solution. This study shows that the water pollution control program: with DHNN and GA optimization in theory, in practice the value of further research and development, has a good prospect. This study for the water pollution control program optimization provides a new method to open up a better way, but also add a new field for the application of the ANN and GA.

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CLC: > Environmental science, safety science > Environmental pollution and its prevention > Water pollution and its control
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