Next Article in Journal
Profound Impacts of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS)
Previous Article in Journal
A Conceptual Time-Varying Flood Resilience Index for Urban Areas: Munich City
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

Study on Optimal Allocation of Water Resources Based on Surrogate Model of Groundwater Numerical Simulation

School of Water Resources and Environment, China University of Geosciences, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Water 2019, 11(4), 831; https://doi.org/10.3390/w11040831
Received: 22 March 2019 / Accepted: 13 April 2019 / Published: 19 April 2019
(This article belongs to the Section Water Resources Management and Governance)
  |  
PDF [4076 KB, uploaded 25 April 2019]
  |  

Abstract

The characteristics of groundwater systems are highly complex. It will take substantial computational resources and running time to optimize a groundwater numerical simulation model. In this study, in order to realize the coupling of simulation and optimization models, the improved backpropagation (BP) neural network was used as a surrogate model of a groundwater numerical simulation; the improved BP neural network was trained with the groundwater level drawdown–pumping volume data output of the simulation model. The method was applied to the water resource optimal allocation in the near future of Wenshang County, Shandong Provence of China. The results show that the water level drawdown output of the improved BP neural network model fits the results of the simulation model well, showing that the improved BP neural network can effectively be the surrogate of a groundwater numerical simulation to be embedded in an optimization model. The improved simulation and optimization technique can make full use of water resources in the whole area. Under an assurance rate of 50%, both water shortage and water shortage rate reduced to zero in the whole area. Under an assurance rate of 75%, water shortage and water shortage rate reduced to about 10% of the conventional scheme, which dramatically improves the comprehensive benefit of the whole area. View Full-Text
Keywords: water resources; surrogate model; improved BP neural network; simulation and optimization technique water resources; surrogate model; improved BP neural network; simulation and optimization technique
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, Y.; Cui, Y.; Shao, J.; Zhang, Q. Study on Optimal Allocation of Water Resources Based on Surrogate Model of Groundwater Numerical Simulation. Water 2019, 11, 831.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top