Advance of Optimization Methods for Identifing Groundwater Pollution Source Porperties
| Periodical | Applied Mechanics and Materials (Volumes 178 - 181) |
|---|---|
| Main Theme | Sustainable Environment and Transportation |
| Edited by | Mingjin Chu, Huizhong Xu, Zhilin Jia, Yun Fan and Jiangping Xu |
| Pages | 603-608 |
| DOI | 10.4028/www.scientific.net/AMM.178-181.603 |
| Citation | Yu Qiao Long et al., 2012, Applied Mechanics and Materials, 178-181, 603 |
| Online since | May, 2012 |
| Authors | Yu Qiao Long, Wei Li, Ju Huang |
| Keywords | Groundwater, Optimization Approach, Pollution, Source Identification |
| Price | US$ 28,- |
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.
