Redesign of Groundwater Monitoring Network Kuhdasht Aquifer

Document Type : Research Article

Authors

1 Associate Professor, Faculty of New Sciences and Technologies, University of Tehran, Iran

2 Msc Student in Ecohydrology, University of Tehran, Iran

Abstract

Long-term groundwater monitoring networks can provide essential information for planning and management water resources. Budget constraints in water resource management agencies often mean a reduction in the number of observation wells included in a monitoring network. Due to economic considerations and reducing monitoring costs, the optimization approach in this study is to reduce the monitoring stations. In the present study, genetic algorithm was used to designing optimal water level monitoring network. The aim of optimization to determine an optimal combination (reduced) from the main network was well observed, so that the possibility of measurement error smallest and least loss of data and provide the best distribution of wells. Using the genetic algorithm, the groundwater monitoring network of the region was optimized. Using the genetic algorithm, the groundwater monitoring network of the region was optimized. In Koohdasht Plain, among of  15 observation wells, 13 wells observations had data. in order to do this research, the data for 36 consecutive months, namely, data from 1392 to 1394 were used. Using the genetic algorithm, the groundwater monitoring network was redesigned and selected wells were selected from potential points. finally, 28 wells had the lowest RMSE of  0.113 and had the best distribution. Location of wells obtained by a number of existing wells was near. The results obtained with the criteria of the quantity of groundwater monitoring network design, which corresponded to show the effectiveness of this approach.

Keywords

Main Subjects

References

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Iranian journal of Ecohydrology
Volume 5, Issue 4
January 2019
Pages 1255-1266

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History

  • Receive Date: 01 May 2018
  • Revise Date: 08 September 2018
  • Accept Date: 16 September 2018
  • First Publish Date: 22 December 2018
  • Publish Date: 22 December 2018

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