Decision support system of regional water resources

被引:0
作者
Zhu C. [1 ]
Hao Z. [2 ]
机构
[1] College of Urban Construction, Hebei University of Engineering, Handan
[2] State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University
关键词
Decision support system; Decision-making variable deduction; Handan; Water resources;
D O I
10.4304/jsw.6.11.2300-2307
中图分类号
学科分类号
摘要
It is very important to dispose the water resources rationally. Rational disposition of water resources is the nucleus of sustainable water resources utilization, and is the important way to solve the district water resource shortage and raises utilization efficiency of water resource. A decision-making variable model is established by groundwater, surface water and precipitation. Water resources managing decision support system in handan is developed on the language of visual basic, with the usage of database-access and the map-object GIS groups. And the system has the functions including managing function of data and files and editing function of images. During the model built, idea of decision-making variable deduction is adopted to transform three kinds of water to get the results of sustainable utilization of water resources. Finally Visual software is programmed Using GIS and VB language, and the rational blue print of water resources in this region can be decided applying optimized model. The system plays an important role in the water resources management. © 2011 ACADEMY PUBLISHER.
引用
收藏
页码:2300 / 2307
页数:7
相关论文
共 22 条
[11]  
Hogg R.V., Tanis E.A., Probability and statistical inference, (1988)
[12]  
Xi C., Chuan-jie L., Zhong-ming H., Et al., Numerical modeling of groundwater in a spring catchment and prediction of variations in the spring discharge[J], Hydrogeology and Engineering Geology, 2, pp. 36-40, (2006)
[13]  
Ju Q., Yu Z., Hao Z., Et al., Division-based Rainfall-Runoff Simulations with BP Neural Networks and Xinanjiang models [J], Neurocomputing, 72, pp. 2873-2883, (2009)
[14]  
Parkin G., Birkinshaw S.J., Younger P.L., Et al., A numerical modelling and neural network approach to estimate the impact of groundwater abstractions on river flows, Journal of Hydrology, 339, pp. 15-28, (2007)
[15]  
Birkinshaw S.J., Parkin G., Rao Z., A hybrid neural networks and numerical models approach for predicting groundwater abstraction impacts, Journal of Hydroinformatics, 10, 2, pp. 127-137, (2008)
[16]  
Demissie Y.K., Valocchi A.J., Minsker B.S., Et al., Integrating a calibrated groundwater flow model with error-correcting data-driven models to improve predictions, Journal of Hydrology, 364, pp. 257-271
[17]  
Garcia L., Shigidi A.A., Using neural networks for parameter estimation in ground water, Journal of Hydrology, 318, pp. 215-231, (2006)
[18]  
McDonald M.C., Harbaugh A.W., A modular three-dimensional finite difference ground-water flow model, U.S. Geological Survey Techniques of Water Resources Investigations[R], (1988)
[19]  
Douglas Dufresne P., Charles Drake W., Regional groundwater flow model construction and wellfield site selection in a karst area, Lake City, Florida [J], Engineering Geology, 52, 1, pp. 129-139, (1999)
[20]  
Chen X., Chen X., Stream Water Infiltration, Bank Storage, and Storage Zone due to Flood Storages in Channels[J], Journal of Hydrology, 280, pp. 246-264, (2003)