Application of New LSSVM-PSO Optimization-Simulation Model in Designing Optimal Groundwater Level Network Monitoring

Document Type : Research Article


1 MSc. Student of sciences and Water Engineering Dept. University of Birjand

2 Associate Professor of sciences and Water Engineering Dept. University of Birand

3 Assistant Professor of sciences and Water Engineering Dept. University of Birand


Monitoring network optimization in water resources, a decision-making process is available for having the best combination in between stations. The study of particle swarm algorithm or PSO algorithm were used for determination of the location and number of network observation wells. The first  using the least squares support vector machine model and input parameters coordinates, evaporation, precipitation last two months, one month before groundwater table  with RBF kernel function was simulated groundwater table. Then the link LSSVM models and PSO model proper location well under two scenarios were determined. In the first scenario was to determine the location of fixed piezometers 42. In the second scenario was considered as a variable number of piezometers. The results of this study showed, Given that our objective function is to minimize the difference between the observed and the simulated, in the first scenario is the least amount of difference in repeat 280 with the objective function 0.9865. The results of the second scenario shows that the number was 28 piezometers Which represents a decrease of 55 percent compared to the initial state is the number of piezometers. In both scenarios, the distribution of points in the southern parts has increased due to the increase in the hydraulic slope and has decreased in the northern parts. In this scenario, the lowest error was repeated 338 with the aim of 0.9145. This optimization at various stages with several stages of trial and error, show the degree of importance and superiority of the latter scenario than the first scenario.


Main Subjects

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Volume 5, Issue 4
January 2019
Pages 1309-1319
  • Receive Date: 21 June 2018
  • Revise Date: 23 September 2018
  • Accept Date: 23 September 2018
  • First Publish Date: 22 December 2018
  • Publish Date: 22 December 2018