Evaluation of different semivariogram models in groundwater quality mapping using GIS-based simple kriging at Khorasan Razavi-Iran

• Main Author: Ng, Zone Fhong
• Format: Undergraduates Project Papers
• Language: English
• Published: 2015
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/12186/1/FKASA%20-%20NG%20ZONE%20FHONG%20%28CD9217%29.pdf; http://umpir.ump.edu.my/id/eprint/12186/

A high demand of the water usage at Khorasan Razavi-Iran was supplied from groundwater. The quality of this groundwater was determined by taking samples from 472 of the wells within the study area like SO4 and pH. This study was about to choose the best fitted semivariogram models to perform the optimum result of the groundwater quality mapping by using point interpolation. In addition, this research also can show about the estimation accuracy versus the lag distance. The GIS-based software, ILWIS 3.4 was used for geostatistical analysis and generation of the groundwater quality map. The pixel size and lag distance used was 100m. An interpolation technique, Simple Kriging (SK), was applied to obtain the spatial distribution of groundwater quality parameters. In the result, the estimated semivariogram values were the best fitted semivariogram models that were Rational Quadratic model for SO4 and the Exponential model for pH. Evaluation of these semivariogram models and the estimation accuracy of SK methods was by Coefficient of Determination (R²), Nash-Sutcliffe Model Efficiency (E), and Root Mean Square Deviation (RMSD). For SO4 in Rational Quadratic model, it was 0.95 for R² which represent the highest value among others model, 0.95 for E which represent the highest also and 1.67 for RMSD which indicate the lowest error from the rest model. On the other hand, the predicted error for pH in Exponential model was 0.78 for R², 0.77 for E and the last was 0.18 for RMSD. Furthermore, the accuracy trend was illustrated in table form and figures that the error of the accuracy is getting worse for example the R² for SO4 in 500m lag with 0.96 was become lesser in 1500m that is 0.69. For the E from 500m to 1500m, the value was lesser as well as RMSD was increased due to the distance. All in all, the objectives were accepted.