Using Regression Kriging to Analyze Groundwater According to Depth and Capacity of Wells
Groundwater is valuable, because it is needed as fresh water for agricultural, domestic, ecological and industrial purposes. However, due to population growth and economic development, the groundwater environment is becoming more and more important and extensive. So the objective of the study is to analyze and predicting groundwater for the year 2018 based on depth and capacity of wells by using the modern style of analyzing and predicting, which is regression kriging (RK) method. Regression kriging is a geostatistical approach that exploits both the spatial variation in the sampled variable itself, and environmental information collected from covariate maps for the target predictor. It is possible to predict groundwater quality maps for areas at Sulaimani governorate in Kurdistan Regions Iraq. Sample data concerning depth and capacity of groundwater wells were collected on Ground Water Directorate in Sulaimani City. The most important result of the study in the regression kriging was the depth and capacity prediction map. The samples from the high depth of wells are located in south of Sulaimani governorate, while the north and middle areas of Sulaimani governorate have got low depths of wells. Although the samples from the high capacity are located in south of Sulaimani governorate, in the north and middle the capacity of wells have decreased. The classes (230 – 482m) of depth are the more area, while the classes (29 – 158G/s) of capacity are the almost area in the study.
Buchanan (1983). Ground Water Quality and Quantity Assessment. J. Ground Water. pp. 193-200.
Chilton J (1992). Women and Water. Waterlines J. 2(110): 2-4. EPA (2005). "Protecting Water Quality from Agricultural Runoff" Fact Sheet No. EPA-841-F-05-001.
Cressie N. (1993). Statistics for Spatial Data. John Wiley & Sons: Hoboken, NJ.
Goovaerts, P., (1997). Geostatistics for Natural Resource Evaluation, Oxford Univ. Press, New York.
Hengl T, Heuvelink GBM, Stein A., (2003). Comparison of kriging with external drift and regression-kriging. Technical Report, International Institute for Geoinformation Science and Earth Observation (ITC), Enschede, Netherlands.
Hengl, T., Heuvelink, G. B. M., & Rossiter, D. G. (2007). About regression-kriging: From equations to case studies. Computers and Geosciences, 33(10), 1301-1315. doi: 10.1016/j.cageo.2007.05.001.
Hengl, T., Heuvelink, G. B. M., & Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120(1-2), 75-93. doi: 10.1016/j.geoderma.2003.08.018.
Hengl, Tomislav; Bajat, Branislav; Blagojević, Dragan; Reuter, Hannes I. (2008). "Geostatistical modeling of topography using auxiliary maps". Computers & Geosciences. 34 (12): 1886–1899. doi:10.1016/j.cageo.2008.01.005.
Huang C. L., Wang H. W., and Hou J. L. (2015). Estimating spatial distribution of daily snow depth with kriging methods: combination of MODIS snow cover area data and ground-based observations, The Cryosphere Discuss., 9, 4997–5020, www.the-cryosphere-discuss.net/9/4997, published by Copernicus Publications on behalf of the European Geosciences Union, doi:10.5194/tcd-9-4997.
Kim Seheon, Park Dongjoo, Heo Tae-Young, Kim Hyunseung and Hong Dahee, (2016). Estimating vehicle miles traveled (VMT) in urban areas using regression kriging, JOURNAL OF ADVANCED TRANSPORTATION J. Adv. Transp. 50:769–785 Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/atr.1374.
Lark, R.M., Cullis, B.R., (2004). Model-based analysis using REML for inference from systematically sampled data on soil. Eur. J. Soil Sci. 55, 799–813.
Lloyd, C.D., (2005). Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain. Journal of Hydrology, 308 (1-4): 128-150.
Matheron, Georges (1969). "Part 1 of Cahiers du Centre de morphologie mathématique de Fontainebleau". Le krigeage universel. École nationale supérieure des mines de Paris.
McBratney, A., Odeh, I., Bishop, T., Dunbar, M., Shatar, T., (2000). An overview of pedometric techniques of use in soil survey. Geoderma, 97:293-327.
Pebesma, Edzer J (2006). "The Role of External Variables and GIS Databases in Geostatistical Analysis". Transactions in GIS. 10 (4): 615–632. doi:10.1111/j.1467-9671.2006.01015.
Webster, R., & Oliver, M. A. (2007). Geostatistics for Environmental Scientists (Second education.). Chichester: Wiley.
Z. S. Han, (2003). Groundwater resources protection and aquifer recovery in China Environmental Geology, vol. 44, pp. 106-111, doi: 10.1007/s00254-002-0705-x.
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