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Advance of Optimization Methods for Identifing Groundwater Pollution Source Porperties

机译:识别地下水污染源特性优化方法的进展

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China has to confront the groundwater resources crisis and the deterioration of groundwater environment. Reinforcing the studies on groundwater pollution source identification (GPSI) could be an important support to contamination removing, groundwater protecting, drinking water security, and development of society and economy. Exploring the new theory and method on GPSI could push the studies on ill-posed problems, and improve the techniques of contamination remediation. GPSI has been studied for thirty years, and a brief review is given to conclude the characteristics of GPSI problems. The mathematical simulation method can be classified into four types: optimization method, analytical and regression method, direct method, and stochastic method. A specific review of optimization approaches is given in this paper. The configuration, simulation procedures, common optimization algorithms used by the optimization methods are discussed in detail. Both non-heuristic and heuristic algorithm can be used to solve the PSI problem. The heuristic algorithm is more suitable for complex numerical and field cases, but it is time-consuming. The non-heuristic algorithm, especially the algorithm combined with analytical method, is time-economical, but is not suitable for complicated numerical and field tests. Further researches may focus on more complex GPSI problems, expressing physical chemistry and biological process, improving efficiency and model uncertainty of GPSI modeling.
机译:中国必须面对地下水资源危机和地下水环境恶化。加强对地下水污染源识别(GPSI)的研究可能是对污染,地下水保护,饮用水安全和社会和经济发展的重要支持。探索GPSI的新理论和方法可以推动对患病问题的研究,提高污染修复技术。 GPSI已经研究了三十年,并介绍了GPSI问题的特征。数学仿真方法可以分为四种类型:优化方法,分析和回归方法,直接方法和随机方法。本文给出了对优化方法的具体审查。详细讨论了优化方法使用的配置,仿真过程,常见优化算法。非启发式和启发式算法都可以用来解决PSI问题。启发式算法更适合复杂的数值和现场情况,但它是耗时的。非启发式算法,尤其是与分析方法相结合的算法是时间经济的,但不适合复杂的数值和现场测试。进一步的研究可以专注于更复杂的GPSI问题,表达物理化学和生物过程,提高GPSI建模的效率和模型不确定性。

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