Optimization of Groundwater Level Monitoring Network Design using Particle Swarm Metaheuristic Method

Document Type : Research Article

Abstract

Considering the complexity of groundwater media and high cost of groundwater monitoring, new innovative methods help us in the improvement of groundwater systems. In this research for minimizing overall data loss in total of optimized network, particle swarm optimization algorithm was used. In order to verify the effectiveness of the algorithm, one monitoring network with 57 observation wells was chosen. Later by reducing the number of monitoring wells to 42, the amount of RMSE was measured. Considering 0.3 as the threshold of error, the optimized network was obtained using the remaining 45 observation wells. A comparison of groundwater contours with original network showed that the solution of proposed algorithm is effective. In the remaining 42 observation wells there were some minor discrepancies. In comparison with genetic algorithm, the solution of PSO algorithm is better and has higher efficiency. Comparison of their RMSE also shows a faster convergence for PSO algorithm. Prediction of groundwater level contours in both models were possible and comparable too. Distribution of the omitted observation wells supported this comparison

Keywords


حسینی عرب، ع.م. 1381. دستور العمل طراحی، پایش و کنترل کمّی و کیفی منابع آب زیرزمینی کشور، وزارت نیرو، سازمان مدیریت منابع آب ایران، معاونت پژوهش، دفتر مطالعات پایه منابع آب.
خورسندی، ا. 1385. بررسی و تکمیل مطالعات بیلان هیدروکلیماتولوژی دشت آستانه-کوچصفهان، شرکت سهامی آب منطقه­ای گیلان.
Aarts, E., and Lenstra, J.K. (1997). Local Search in Combinatorial Optimization. John Wiley & Sons, Chichester, England.
Bellmore, M., and Nemhauser, G.L. (1968). "The traveling salesman problem: A survey". Operations Research, 16: 538-582.
Chau, K., (2004). "River stage forecasting with particle swarm optimization", Springer-Verlag, Berlin Heidelberg, LNAI 3029: 1166-1173.
Cieniawski, S.E., Eheart, J.W., and Ranjithan, S., (1995). "Using genetic algorithm to solve a multiobjective groundwater monitoring problem". Water Resour. Res. 31 (2): 399-409.
Clerc, M., (2004). "Discrete particle swarm optimization, illustrated by the traveling salesman problem". In B. V. Babu & G. C. Onwubolu (Eds.), New optimization technique in engineering, Berlin: Springer, 219-239.
Clerc, M., (2006). Particle swarm optimization. London: ISTE.
Glover, F., Laguna, M., and Mart, R. (2000). "Fundamentals of scatter search and path relinking". Control and Cybernetics, 29(3): 653-684.
Goldbarg, E.F., Souza, G.R., and Goldbarg, M.C. (2006). "Particle swarm for the traveling salesman problem'. Springer-Verlag Berlin, LNCS 3906: 99-110.
Izquierdo, J., Montalvo, I., Perez, R., and Fuertes, V.S. (2008). "Design optimization of wastewater collection networks by PSO", Computers and Mathematics with Applications, 56(3): 777-784.
Jousma, G. (2008). "Guideline on groundwater monitoring for general reference purposes". International Groundwater Resource Assessment center. International Working Group I, GP 2008-1. Utrecht. p. 165.
Li, Y., and Chan Hilton, A.B., (2006). "Reducing spatial sampling in long-term ground-water monitoring using ant colony optimization". International Journal of Computational Intelligence Research 1 (1): 19-28.
 
Montalvo, I., Izquierdo, J., and Perez, R. (2008). "Particle swarm optimization applied to the design of water supply systems", Computers and Mathematics with Applications, 56(3): 769-776.
Moraglio, A., Di Chio, C., and Poli, R. (2007). "Geometric particle swarm optimization". Proceedings of European Conference on Genetic Programming (EuroGP), Berlin: Springer, 125-136.
Nielsen, D. (1991). Practical Handbook of Groundwater Monitoring, Lewis Publisher, Chelsea Michigan, USA, Briefing note Series. CRC Press. p. 728.
Nunes, L.M., Cunha, M.C., and Ribeiro, L., (2004). "Groundwater monitoring network optimization with redundancy reduction". Journal of Water Resource Planning and Management 130 (1): 33-43.
Poli., R. Kennedy., J. and Blackwell., T. (2007). "Particle swarm optimization. An overview". Swarm Intelligence. Berlin: Springer, 33-57.
Reed, P.M., Minsker, B.S., and Valocchi, A.J., (2000). "Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation". Water Resour. Res. 36 (12): 3731 – 3741.
Suribabu., C.R., and Neelakantan., T.R. (2006). "Design of water distribution networks using particle swarm optimization". Urban Water Journal. 3(2): 111-120.
Tuinhof, A. (2002). "Groundwater monitoring requirements for managing aquifer response and quality threats". Sustainable Groundwater management: Concept and Tools, GW Mate, Briefing Report. No, 9. World Bank.
Wegley, C., Eusuff, M., and Lansey, K., (2000). "Determining pump operations using particle swarm optimization". In: Proceedings of Joint Conference on Water Resources Engineering and Water Resources Planning and Management, Minneapolis, MN, July 30-Aug 2, 2000. ASCE, Reston, VA.
Wu, J., Zheng, C., and Chien, C.C., (2005). "Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions". Journal of Contaminant Hydrology 77: 41-65.
  • Receive Date: 28 February 2015
  • Revise Date: 28 September 2015
  • Accept Date: 10 October 2015
  • First Publish Date: 10 October 2015