Minimization of Groundwater Observation Wells Using Geostatistics and Optimization Technique (Case study: Dezfoul-Andimeshk plain)

Document Type : Complete scientific research article

Authors

1 Shahrekord University

2 Chamran

3 Khouzestan Water and Power Authority

Abstract

Abstract
Background and objectives: Groundwater is one of the most valuable water resources owing to its quantity and quality. Measuring water level is a basic and essential step in any groundwater study. Since the measured data are derived only from a limited number of observation points and, at the same time, they must be extended to the whole surface of the zone, it is essential to determine optimal location of measuring network. Accordingly, for reduction of observation wells, a number of them were eliminated in such a way that the remaining wells have an optimal combination.
Materials and methods: Dezfoul-Andimeshk plain, located in north of Khouzestan Province, was investigated as a case study. Kriging, as the best linear unbiased estimator, was used for interpolation of groundwater level. Based on the measured data of 76 studied observation wells, a theoretical variogram was fitted to empirical variogram points. To achieve the optimal combination, tabu search algorithm (a meta-heuristic algorithm) was used. Two computer programs including GSLIB and MATLAB were used for Kriging and tabu search, respectively. Linking the programs and preparing conditions for data exchange between them, a model called optimizer model was generated so that capability of optimizing the groundwater surface measuring network is made possible. The observation points are distributed such that variance estimation error limited to the plain extent is minimized.
Results: Optimization model is run in five different cases. For the first to third cases, simply considered for verification purposes, optimal selection of one to three observation wells were respectively presented, which authenticated the model performance against complete search method. In the fourth and fifth cases, the optimal selection of 50 and 60 out of 76 observation wells were presented for which the groundwater levels were compared and the results show that there exists a good match between the groundwater surface results in comparison with those of whole wells (76 wells).
Conclusion: As the variogram fitted to the groundwater data is isotropic, the distribution of the observation wells along different directions will be the same. This is tangible for the first to third cases in which the number of wells is small. Optimization results of 50 and 60 observation wells were automatically achieved by the model without any interference. The comparisons of results indicate a good accuracy for the optimization model. Also, the results show that using the present model saves a lot of time.

Keywords: Groundwater, Monitoring network, Geostatistics, Optimization, Tabu search

Keywords

References

1.http://water.usgs.gov/edu/watercycle.html. The Water Cycle - USGS Water Science School. United States Geological Survey (USGS).
2.Hassani Pak, A.A. 2008. Geostatistics. Tehran University, Tehran, Iran, 314p.
3.Liu, S., Mo, X., Li, H., Peng, G., and Robock, A. 2001. Spatial Variation of Soil Moisture in China: Geostatistical Characterization. J. Meteorol. Soc. Japan. 79: 1B. 555-574.
4.Sharma, M.L., Gander, G.A., and Hunt, C.G. 1980. Spatial variability of infiltration in a watershed. J. Hydrol. 45: 1-2. 101-122.
5.Sun, W., and McBratney, A. 2012. Analysis and prediction of soil properties using local regression-kriging. Geoderma. 171: 1. 16-23.
6.Cheng, K.-S., Wei, C., Cheng, Y.-B., and Yeh, H.-C. 2003. Effect of spatial variation characteristics on contouring of design storm depth. Hydrological Processes. 17: 9. 1755-1769.
7.Pardo-Igúzquiza, E. 1998. Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing. J. Hydrol. 210: 1-4. 206-220.
8.Kebaili Bargaoui, Z., and Chebbi, A. 2009. Comparison of two kriging interpolation methods applied to spatiotemporal rainfall. J. Hydrol. 365: 1-2. 56-73.
9.Asakereh, H. 2008. Kriging Application in Climatic Element Interpolation, A Case Study: Iran Precipitation. Geograph. Dev. Iran. J. 6: 12. 25-42. (In Persian)
10.Tsintikidis, D., Georgakakos, K.P., Sperfslage, J.A., Smith, D.E., and Carpenter, T.M. 2002. Precipitation Uncertainty and Raingauge Network Design within Folsom Lake Watershed.
J. Hydrol. Engin. 7: 2. 175-184.
11.Barca, E., and Passarella, G. 2008. Spatial evaluation of the risk of groundwater quality degradation. A comparison between disjunctive kriging and geostatistical simulation. Environmental Monitoring and Assessment. 137: 1-3. 261-273.
12.Barca, E., Bruno, D.E., and Passarella, G. 2016. Optimal redesign of environmental monitoring networks by using software MSANOS. Environmental Earth Sciences. 75: 14. 1082.
13.Ghahraman, B., Hosseini, S.M., and Asgari, H.R. 2003. Use of Geostatistics in Evaluation of Groundwater Quality Monitoring Network. Amirkabir J. Sci. Res. 30: 1. 971-981. (In Persian) 
14.Karamouz, M., Kerachian, R., Akhbari, M., and Hafez, B. 2009. Design of River Water Quality Monitoring Networks: A Case Study. Environmental Modeling and Assessment.
14: 6. 705-714.
15.Ben-Jemaa, F., and Mariño, M.A. 1990. Optimization of a Groundwater Well Monitoring Network. Optimizing the Resources for Water Management, Proceeding Paper, American Society of civil Engineers (ASCE), Pp: 610-614.
16.Prakash, M.R., and Singh, V.S. 2000. Network design for groundwater monitoring - a case study. Environmental Geology. 39: 6. 628-632.
17.Kumar, V., and Remadevi. 2006. Kriging of Groundwater Levels – A Case Study. J. Spatial Hydrol. 6: 1. 12.
18.Nikroo, L., Kompani-Zare, M., Sepaskhah, A.R., and Fallah Shamsi, S.R. 2010. Groundwater depth and elevation interpolation by kriging methods in Mohr Basin of Fars province in Iran. Environmental Monitoring and Assessment. 166: 1-4. 387-407.
 19.Noori, S.M., Ebrahimi, K., Liaghat, A.M., and Hoorfar, A.H. 2013. Comparison of different geostatistical methods to estimate groundwater level at different climatic periods. Water Environ. J. 27: 1. 10-19.
20.Zamani, R., Akhondali, A.M., Zarei, H., and Radmanesh, F. 2014. Estimation of the groundwate level by using a combined optimized method with Genetic Algorithms in Ramhormoz plain. Irrig. Water J. 4: 15. 26-38. (In Persian) 
21.Mirzaei, N., Maroofpour, S., and Dinpashoh, Y. 2016. Estimation of the Groundwater Level using the Geostatistics; Case Study Tabriz plain. 5th Comprehensive Water Resources Management Conference, Pp: 1-9. (In Persian) 
22.Zamani, R., Akhond-Ali, A.M., and Zarei, H. 2017. An application of combined geostatistics with optimized artificial neural network bygenetic algorithm in estimation of groundwater Level (Case study: Dezful and Zeidoon plains). Irrigation Sciences and Engineering.
40: 2. 27-37. (In Persian) 
23.Kord, K. 2017. Modeling of groundwater level fluctuations in Interaction with Dez river. M.Sc. Thesis, Khoramshahr Marine Science and Technology University, Khoramshahr, Iran, 86p. (In Persian) 
24.Lee, Y.-M., and Ellis, J.H. 1996. Comparison of Algorithms for Nonlinear Integer Optimization: Application to Monitoring Network Design. J. Environ. Engin. 122: 6. 524-531.
25.Nunes, L.M., Cunha, M.C., and Ribeiro, L. 2004. Groundwater Monitoring Network Optimization with Redundancy Reduction. J. Water Resour. Plan. Manage. 130: 1. 33-43.
26.Safari, M. 2002. Determination of Optimal Groundwater Piezometric Network Using Geostatistics Methods. M.Sc. Thesis, Tarbiyat Modares University, Tehran, Iran, 135p.
(In Persian) 
27.Dehghani, A.A., Asgari, M., and Mosaedi, A. 2009. Comparision of Geostatistics, Artifitial Neural Networks and Adaptive Neuro-Fuzzy Inference System Approaches in Groundwater Level Interpolation, Case study: Ghazvin Aquifer. J. Agric. Sci. Natur. Resour. 16: 1-b. 517-529.
28.Nikroo, L., Kompani-Zare, M., and Sepaskhah, A.R. 2009. Optimization of groundwater level measuring network using geostatistics, A case study: Mohr basin in Fars Province. 3th Iranian Water Resources Management Conference, Tabriz, Iran, Pp: 1-9. (In Persian)
29.Ganji Khoramdel, N., Keykhaei, F., Mohammadi, K., and Monem, M.J. 2015. Optimization of Groundwater Elevation Monitoring Network Using Particle Swarm Optimization Technique. J. Hydr. 3: 1. 25-35. (In Persian)
30.Mirzaie-Nodoushan, F., Bozorg-Haddad, O., and Loaíciga, H.A. 2017. Optimal design of groundwater-level monitoring networks. J. Hydroinf. 19: 6. 920-929.
31.Glover, F. 1990. Tabu search: A tutorial. Interfaces. 20: 4. 74-94. 
Journal of Water and Soil Conservation
Volume 25, Issue 3
July and August 2018
Pages 79-96

Files

Share

How to cite

Statistics