Comparing two data driven interpolation methods for modeling nitrate distribution in aquifer

As a soluble compound in water, nitrate could easily pass through soil to the groundwater. In recent decades, nitrate pollution of groundwater has been increased mainly as a result of excessive application of fertilizers in agricultural areas. Appraisal of nitrate distribution in aquifers is not a new problem but it is still unsolved. This paper compares the performances of two modeling approaches such as geostatistical (kriging) and soft (fuzzy) computing in spatial interpolation of nitrate concentration in groundwater. For this purpose, the groundwater samples are collected from springs and wells in Mersin–Tarsus Aquifer to be considered. The estimation models are established based on data driven modeling concept. The results and performance evaluations indicate that the estimation capacity of the fuzzy model is higher than that of the kriging model.