Using of moran spatial autocorrelation index and bayesian Kriging in groundwater quality assessment (Case study: Azarshahr plain)

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Docheshmeh Gorgij A.; ASGHARI MOGHADDAM A.

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Paper Information

Journal: IRANIAN WATER RESEARCH JOURNAL Year:2019 | Volume:13 | Issue:2 (33) Page(s): 21-31

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Title

Using of moran spatial autocorrelation index and bayesian Kriging in groundwater quality assessment (Case study: Azarshahr plain)

Author(s)

Docheshmeh Gorgij A. | ASGHARI MOGHADDAM A. | Issue Writer Certificate

Keywords

Azarshahr

Cross validation

Empirical Bayesian Kriging (EBK)

Moran Spatial Autocorrelation Index

Abstract

The present study demonstrates a method in groundwater quality assessment using the Empirical Bayesian Kriging and Moran Spatial Autocorrelation Index. In this study, concentration of Arsenic, Lead, Iron, Manganese and Nitrate and also groundwater table and Total Dissolved Solid has been measured for 21 point in Azarshahr Plain (East Azerbaijan). Azashahr study area is one of the Lake’ s twelve adjacent aquifers that is located between 45° , 46’ to 45° , 50’ longitudinal and 37° , 43’ to 37° , 52’ latitudinal. Its total area is about 457 km2, that its plain has an area about 124 km2. The highest and lowest heights in the study area are 3700m and 1282m, respectively. Its average annual precipitation is about 221. 2 mm whereas the average annual evaporation is about 1579 mm. The most important stream in Azarshahr Plain is Azarshahrchai which has a southeast-northwest trend and is eliminated before reaching to the lake because of wide agricultural usage. On the other hand, the total annual discharge of aquifer is about 90. 64 million that is one of the groundwater depletion and decreasing the quality of groundwater factors in the study area. Moran’ s I is a commonly used indicator of spatial autocorrelation. In this study, the Moran’ s I was used as the ﬁ rst measure of spatial autocorrelation. Its value ranges from − 1 to 1. The value “ 1” means perfect positive spatial autocorrelation (high values or low values cluster together), while “ − 1” suggests perfect negative spatial autocorrelation (a checkerboard pattern), and “ 0” implies perfect spatial randomness. After that, the appropriate weight has given to the aforementioned parameters, considering the international standard of water quality and spatial autocorrelation index of each of them. After determining the layer rules, the Expert Choice software was applied to calculate the comparing binary matrix of analytic hierarchy process. After that, the final weight for each layer with inconsistency of 0. 08 was derived that is less than 0. 1 and acceptable. In the conventional geostatistical approaches for interpolation and kriging, the variance structure is estimated first, and then the estimated variance is used for interpolation that whereas a Bayesian approach to the interpolation of spatial processes will provide a general methodology for taking into account the uncertainty about parameters on subsequent predictions. The Bayesian approach generalizes automatically to the case which the variogram parameters are unknown, whereas the classical approach essentially makes the assumption that these are known and only deals with the question of uncertainty of model parameters in a very peripheral way. Then replacing the popular interpolation methods, the Empirical Bayesian Kriging prediction method has utilized to expand every parameter to the whole plain. In order to evaluate the prediction results, the Cross validation method was used. The study area was divided to 4 sections, as desirable, acceptable, moderate and non-acceptable. The final obtained map reveals that desirable quality is just located in the southeast of the study area in the upstream of the groundwater input. The acceptable quality of the groundwater is located in the east and southeast of the study area. The center, west and northwest of the study area has a moderate quality. The groundwater in the north, northwest and southwest of the study area has a non-acceptable quality that seems due to anthropogenic activities, especially agricultural and industrial during the recent years. On the other hand, the spatial autocorrelation model of effective parameters on water quality in the on hand and Bayesian kriging method with its precise assessment and prediction in some areas without data have a high applicability. The Cross validation technique in model accuracy approving, is a valuable tool. Every three methods played an important role in modifying and improving the analytic hierarchy process of the groundwater quality assessment in the study area. The resulted map revealed that the groundwater quality of the east and southeast of the study area are desirable and acceptable with about 32. 53 and 44. 38 km2, respectively. The center and west section with area about 69. 32 and the north and southwest with area about 27. 21 km2 have a moderate and non-acceptable quality, respectively. Upon to the analytic hierarchy process of the groundwater quality assessment of the Azarshahr Plain, it has been revealed that 16 percent of aquifer has non-acceptable quality, about the 40 percent has moderate quality and other has an acceptable to desirable quality in the study area. The result of this study has shown the necessity of the groundwater quality precise monitoring in the study area.

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APA: Copy

Docheshmeh Gorgij, A., & ASGHARI MOGHADDAM, A.. (2019). Using of moran spatial autocorrelation index and bayesian Kriging in groundwater quality assessment (Case study: Azarshahr plain). IRANIAN WATER RESEARCH JOURNAL, 13(2 (33) ), 21-31. SID. https://sid.ir/paper/739892/en

Vancouver: Copy

Docheshmeh Gorgij A., ASGHARI MOGHADDAM A.. Using of moran spatial autocorrelation index and bayesian Kriging in groundwater quality assessment (Case study: Azarshahr plain). IRANIAN WATER RESEARCH JOURNAL[Internet]. 2019;13(2 (33) ):21-31. Available from: https://sid.ir/paper/739892/en

IEEE: Copy

A. Docheshmeh Gorgij, and A. ASGHARI MOGHADDAM, “Using of moran spatial autocorrelation index and bayesian Kriging in groundwater quality assessment (Case study: Azarshahr plain),” IRANIAN WATER RESEARCH JOURNAL, vol. 13, no. 2 (33) , pp. 21–31, 2019, [Online]. Available: https://sid.ir/paper/739892/en

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