In many environmental applications involving spatial and spatio-temporal data, covariance functions are the fundamental tools for modeling dependent data which observed over space and/or time. Constructing nonseparable spatio-temporal var- iogram or covariance functions is one of the issues related to spatio-temporal geo- statistics for space-time data. Limitations on the number of locations and high values of parameters motivate the need for practical and efficient method to esti- mate the parameters of covariance models for spatial interpolation. In this paper, we discuss different types of spatio-temporal data, also we combine these methods with bees algorithm to explore the spatio-temporal correlation structure of monthly ground-water data in Ilam city. Finally, we predict the level of some spatial stations using spatio-temporal kriging in which the efficiency of model was assessed by mean of kriging standard deviations.
