A Design Of The Optimized Monitoring Scheme To The Drinking Water Resource Along River

Groundwater dynamic monitoring well network to optimize the design, is less a prerequisite for the use of human and material resources in order to obtain a sufficient number and with a higher accuracy of groundwater hydrogeological information and data for the purpose of design the spatial distribution of groundwater monitoring well networkprograms. Therefore, how to design a groundwater monitoring well network space distribution program, how to adjust the monitoring time and frequency, hydrogeological work to study basic questions and concerns. Existing groundwater monitoring well network monitoring frequency, but many on the history and technical conditions causes the density of some of the monitoring network is too large, but monitoring is frequent; or some of the monitoring network irrational distribution of very small density, but is very low. These questions need to be optimal adjustment of the existing monitoring well network.In China, many parts of the existing groundwater monitoring network at the same time this bears the dual role of monitoring water quality and water level, therefore, to adjust the use of groundwater monitoring network optimization, we must also consider the monitoring well network access to the groundwater levelvalue, the value of water quality monitoring accuracy as good as possible. At present, the optimal adjustment of the groundwater monitoring well network include:Hydrogeological analytic method, Clustering, analysis method, Kalman filter method, on Kriging method and so on.Kriging for solving linear, unbiased, minimum interpolation to estimate the amount of. Kriging is an important theory in geostatistics, Kriging can make full use of monitoring data and the structural properties of semi-variogram sampling point for non-regionalized variables interpolation ways. Compared with the normal interpolation method, its advantages are:the fullest possible use of a variety of hydrogeological information provided in the original data. Kukrit gold variance is the minimum variance estimation of the Kukrit gold, the size of its value to reflect the space well network on the Kukrit gold is estimated to affect the size of the kriging variance is smaller, the spatial layout of the well network is more reasonable; and g The Lijin variance is larger, the program of the current monitoring well network distribution unreasonable need to further adjust its distribution. Thus, the variance of Kukrit gold can actually evaluate the current monitoring network spatial distribution standard.Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation) in1995by Dr. Jamaes Kennedy, and Dr RCEberhart. The algorithm based on swarm intelligence, is a genetic algorithm similar to the intelligent optimization algorithms. But the relative genetic algorithms, particle swarm optimization algorithm is easier to understand and realize, and thus won the recognition of many scholars. Particle swarm optimization (PSO) at home and abroad in recent years more and more detailed, and extended to multi-objective optimization problem and achieved good results.LiGuanpu water source location in the southwest of Shenyang, north shore of hunhe River floodplain areas. Covering an area of about37.88km2and is a typical Riverside groundwater. Average altitude of50meters below the water source, is a relatively flat plain topography areas. Existing wells within the study area48, and distribution of the monitoring network is obviously in uneven, South of dense, sparse on the north side. The current situation also reflects the current pattern in the general problem of groundwater monitoring network.LiGuanpu water sources for the study of this article, using Kriging method for research, and kriging variance as evaluation criteria, using Particle Swarm Optimization algorithm to get2more reasonable optimization. First species, gave up original monitoring network again dug monitoring network system, can sharply upgrade monitoring network are information of reliability, but takes long, and costs high; second species programme, in existing monitoring network Foundation Shang gave up which of14eye well, only on which specified of34eye well for monitoring, is can in does not effect existing monitoring precision of conditions Xia, save human, and material resources.Characteristics and innovations of this article is that (1) for Riverside groundwater characteristics, kriging variance for the optimal objective function of different pollutants.(2) using Particle Swarm Optimization algorithm, identified a new monitor well spacing distribution solution and the use of discrete Particle Swarm algorithm, an improvement was made on the basis of the existing monitoring network optimization of the existing networks.