Evaluation and Optimization of Rain Gauge Network Based on the Geostatistic Methods and Firefly Algorithm. (Case study: Eastern Basin of Urmia Lake)

Document Type : Research Paper

Authors

1 MSc, Department of Water Engineering, Tabriz University

2 Professor, Department of Water Engineering, Tabriz University.

3 Assistant Professor, Department of Water Engineering, Tabriz University.

Abstract

Rainfall is the main motivator in the hydrologic cycle of the basin and it is an element of meteorological phenomena undergoing severe changes in time and place. The suitability of density and distribution of rain gauges in the rain gauge networks of each area is an effective step in the success of the water plans, regional projecting and effective use of the information (Karamouz et al., 2010). Many researchers have shown that the geostatistical prediction method provides better estimates of the regional rainfall than the traditional methods. Tanaka and Putthividhya (2013) used the geostatistical method to assess the quality of the rainfall estimation in the Basin of Chao Phraya. They tried to calculate the difference between the rainfall data and the results obtained from the above methods by plotting Thiessen Network and the co-ordinate lines by nverse Distance Weighting and Ordinary Kriging methods. They also examined the correlation between the height, humidity and temperature with the recorded rainfall values. The findings showed that height had a significant correlation with Monsoon rainfall, while humidity and temperature correlated with the monthly rainfalls. Yang and He (2013), using the super innovative firefly algorithm concluded that this algorithm is more  suitable than the optimal search strategy. Considering the problems of Urmia Lake located in the northwest of Iran, comprehensive studies with an inclusive approach to the problems in this basin are considered necessary. Indeed, it is necessary to concentrate more on the process used in the design of the rain gauge networks and begin to redesign the existing networks in order to refine and complete them.

Keywords

Main Subjects


1-    Abedini, M.J. and Shaghaghian, M.R., 2009. Exploring scaling laws in surface topography. Chaos, Solitons & Fractals42(4), pp.2373-2383 (In Persian).
 
2-    Barca, E., Passarella, G. and Uricchio, V., 2008. Optimal extension of the rain gauge monitoring network of the Apulian Regional Consortium for Crop Protection. Environmental Monitoring and Assessment145(1-3), pp.375-386.
 
3-    Cheng, K.S., Lin, Y.C. and Liou, J.J., 2008. Rain‐gauge network evaluation and augmentation using geostatistics. Hydrological Processes: An International Journal22(14), pp.2554-2564.
 
4-    Ghajarnia, N., Liaghat, A. and Arasteh, P.D., 2015. Comparison and evaluation of high resolution precipitation estimation products in Urmia Basin-Iran. Atmospheric Research, 158, pp.50-65 (In Persian).
 
5-    Ghahraman, B. and Sepaskhah, A., 2001. Autographic rain-gagenetwork design for iran by kriging. Thesis, Shiraz University, Iran. 141p. (In Persian).
 
6-    Hassanzadeh, T., Meybodi, M.R. and Mahmoudi, F., 2011. An improved Firefly Algorithm for optimization in static environment. In Fifth Iran Data Mining Conference/IDMC (In Persian).
 
7-    Hasani, A, 1999, Geostatic, Publishing and Printing University of Tehran, 260 (In Persian).
 
8-    Haberlandt, U., 2007. Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event. Journal of Hydrology, 332(1), pp.144-157.
 
9-    Karamouz, M., Falahi, M. and Nazif, S., 2010. Analysis of Spatial Variation of Precipitation: Comparison of Conventional and Kriging Methods Iran-Water Resources Research, 6(1), pp.1-9 (In Persian).
 
10-  López‐Moreno, J.I. and Nogués‐Bravo, D., 2006. Interpolating local snow depth data: an evaluation of methods. Hydrological Processes: An International Journal, 20(10), pp.2217-2232.
 
11- Mahmoudi-Meimand, H., Nazif, S., Ali Abbaspour, R. and Faraji Sabokbar, H., 2016. An algorithm for optimisation of a rain gauge network based on geostatistics and entropy concepts using GIS. Journal of Spatial Science, 61(1), pp.233-252 (In Persian).
 
12- Mashal, M., Darvishi, E. and Rahimikhoob, A., 2008. Optimizing the Raingages Networks Using Geostatistical Method Case Study: Khozestan Province. Iranian Journal of Irrigation and Drainage. 2 (2), pp. 43-51
 
13- Papamichail, D.M. and Metaxa, I.G., 1996. Geostatistical analysis of spatial variability of rainfall and optimal design of a rain gauge network. Water resources management10(2), pp.107-127.
 
14- Putthividhya, A. and Tanaka, K., 2012. Optimal rain gauge network design and spatial precipitation mapping based on geostatistical analysis from colocated elevation and humidity data. International Journal of Environmental Science and Development3(2), p.124.
 
15- Safarrad, T., Farji, S.H., Azizi, G. and Abbaspour, R., 2013. Spatial Analysis of Precipitation Variations in Middle Zagros Using Geo-Statistical Methods (1995-2004) (In Persian).
 
16- Yang, X.S., 2009, October. Firefly algorithms for multimodal optimization. In International symposium on stochastic algorithms (pp. 169-178). Springer, Berlin, Heidelberg.
 
17- Yang, X.S. and He, X., 2013. Firefly algorithm: recent advances and applications. International Journal of Swarm Intelligence, 1(1), pp.36-50.
 
18- Yazdani, N.M., Sequerloo, A.Y. and Panahi, M.S., 2013. Reduction of Harmonic in Multilevel Inverters using FA and LAFA ALGORITHMS. In J. Basic. Appl. Sci. Res. (Vol. 3, No. 1s, pp. 130-135). TextRoad Publication.
Volume 42, Issue 4
December 2019
Pages 153-166
  • Receive Date: 23 January 2018
  • Revise Date: 14 April 2018
  • Accept Date: 18 April 2018
  • Publish Date: 22 December 2019