Yousefi H, Nohegar A, Khosravi Z, Azizabadi Farahani M. Drought Modeling and Management Using SPI and RDI Indexes (Case study: Markazi provin). The Univercity of Tehrans Scientific Journals Database. 2015; 2(3): 337- 344. (In Persian)
 Mohammadi M, Karami H, Farzin S, Farokhi A. Prediction of Monthly Precipitation Based on Large-scale Climate Signals Using Intelligent Models and Multiple Linear Regression (Case Study: Semnan Synoptic Station), The Univercity of Tehrans Scientific Journals Database. 2017; 4(1): 201- 214. (In Persian)
 Sajal KA, Abdullah GY, Nitin M. Optimal design of rain gauge network in the Middle Yarra River catchment, Australia. Hydrol.Process.2014.
 Pardo-Igúzquiza E. Optimal selection of number and location of rainfall gauges for areal rainfall estimation using geostatistics and simulated annealing. Journal of Hydrology. 1998; 210: 206–220.
 Xie, Y., bin Chen, T., Mei Lei, Jun Yang, Qing-jun Guo, Bo Song, Xiao-yong Zhou, 2011, Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis, Chemosphere, Vol. 82, No. 3, PP. 468- 476.
 Kassim AHM, Kottegoda NT. Rainfall network design through comparative kriging methods. Hydrological Sciences Journal. 1991; 36: 223–240.
 Mohd Aziz M, Yusof F, Mohd Daud Z, Yusop A, Afif M. Optimal design of rain gauge network in Johor by using geostatistics and particle swarm optimization. International Journal of GEOMATE. 2016; 11(25): 2422-2428.
 Adhikary SK, Yilmaz AG, Muttil N. Optimal design of rain gauge network in the Middle Yarra River catchment, Australia. HYDROLOGICAL PROCESSES. 2014; 29(3): 2582–2599.
 Mishra AK, Coulibaly P. Developments in hydrometric network design: a review. Reviews of Geophysics. 2009. 47: RG2001.
 Chebbi A, Bargaoui ZK, da Conceição Cunha M. Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results. Hydrol. Earth Syst. Sci.2013; 17: 4259–4268.
 Tsintikidis D, Georgakakos KP, Sperfslag JA, Smith DE, Carpenter TM. Precipitation Uncertainty and Raingage Network Design Within Folsum Lake watershed. Journal of Hydrologic Engineering.2002; 7(2): 175-184.
 Dimitris M, Metaxa G. 2006. Geostatiscal Analisis of Spatial Variability of Rainfall and Optimal Design of a Rainguage Network, Water Resources Management. 2006; 10: 107-127.
 Cheng KS, Wei C, Cheng YB, Yeh HC. Effect of spatial variation characteristics on contouring of design storm depth, Hydrol Process 2003; 17(9): 1755-69.
 Barca E, Passarella G, Uricchio V. 2008. Optimal Extension of the Rain Gauge Monitoring Network of the Apulian Regional Consortium for Crop Protection, Environ Monittoring Assessment. 2008; 145(58): 375-386.
 Karamouz M, Kerachian R, Akhbari M, Hafez B. Design of river water quality monitoring networks: a case study, Environ Model Assess 2009; 14(6): 705-14.
 Shafiei M, Ghahraman B, Saghafian B. 2013. Evaluation and optimization of raingauge network based on probability kriging (case study: Gorgan-Rud watershed). Iran-Water Resources Research. 2013; 9(2): 9-18. (In Persian)
 Adib A, Moslemzadeh M. Optimal Selection of Number of Rainfall Gauging Stations by Kriging and Genetic Algorithm Methods. International Journal of Optimization in Civil Engineering. 2016; 6(4):581-594.
 Feki H, Slimani M, Cudennec CH. 2016. Geostatistically based optimization of a rainfall monitoring network extension: case of the climatically heterogeneous Tunisia. Hydrology Research.2016; 48(1).
 Asakere H, Kriging interpolation method is used in the case of Iran, the interpolation of precipitation 12/26/1376. Journal of Geography and Development. 2004; 5(12): 25-42. (In Persian)
 Gurunathan K, Ravichandran S. 1994. Analysis of water quality data using a multivariate statistical technique - a case study. IAHS Pub. 1994; 219.
 Salah H. Geostatistical analysis of groundwater levels in the south Al Jabal Al Akhdar area using GIS. GIS Ostrava. 2009; 25: 1-10.
 Taghizadeh R, Zareian M, Mahmudi SH, Heidari A, Sarmadian F. Evaluation methods temporal interpolation to determine the spatial variability of water quality characteristics of groundwater in Rafsanjan. Watershed Management Science and Engineering Iran. 2008; 2(5): 63-70. (In Persian)
 Bastin G, Lorent B, Duque C, Gevers M. Optimal estimation of the average areal rainfall and optimal selection of rain gauge locations, Water Resour Res. 1984; 20(4): 463-70.
 Webster R and Oliver MA. Geostatistics for Environmental Scientists. John Wiley and Sons, Ltd., Chichester, UK.2001; 271.
 Asghari moghadam A, Nurani V, Nadiri A. Predict when and where the groundwater level in the city of Tabriz metro area using neural kriging model. Iran Water Resources Research. 2008; 13(1): 14-24. (In Persian)
 Isaaks, E.H., Srivastava, R.M., 1989, An Introduction to Applied Geostatistic, Oxford University Press New York, P.561.
 Sun Y, Kang S, Li F and Zhang L. Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin oasis of northwest China. Environmental Modelling & Software. 2009; 24:1163-1170.
 28. Camdevyren HN, Demyr A, Kanik S, Keskyn. “Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs.” Ecol. Model. 2005; 181: 581–589.
 Manly BFJ. Multivariate statistical methods: A primer, 2nd Ed., Chapman and Hall, London.1986.
 Lu, W. Z., W. J. Wang, X. K. Wang, Z. B. Xu and A. Y. T. Leung. “Using improved neural network to analyze RSP, NOX and NO2 levels in urban air in Mong Kok, Hong Kong.” Environmental Monitoring and Assessment. 2003; 87: 235–254.
Petersen W. Process identification by principal component analysis of river water-quality data. Ecol. Model.2001; 138: 193-213