## 1 Introduction  
Precipitation data analysis plays a major role in the process of making informed decisions for water resources management. Such data are gathered at monitoring stations scattered throughout a region. They are either not readily available for other locations or may be missing at certain time periods. These issues have negative influences on hydrological studies that rely on precipitation data as input. Therefore, spatial interpolation methods prove to be useful for estimating precipitation data at unsampled locations [1]. Spatial interpolation methods are commonly used for the prediction of the values of environmental variables. Their specificity comes from incorporating information related to the geographic position of the sample data points. The most used methods are usually classified in the categories [2,3]: (1) deterministic methods (Nearest Neighbor, Inverse Distance Weighting (IDW), Splines, Classification and Regression methods), (2) geostatistical methods (Kriging (KG)) or (3) combined methods (regression combined with other interpolation methods and classification combined with other interpolation methods). The importance of a successful spatial interpolation of precipitation before the hydrological modeling and a thorough review of different spatial interpolation methods for its modeling are presented in [1]. Among them, IDW stands out as a real competitor. It builds estimates for environmental variables at a location, based on the values of those variables at some nearby sites and on the distances between that location and those where the variable is known. The interpolation method often chosen by geoscientists is IDW, implemented in many Geographic Information Systems (GIS) packages [4]. Its popularity is mainly due to its straightforward interpretability, easy computability and good prediction results. Still, the No Free Lunch theorem for optimization [5] stands also in the case of spatial interpolation: no method stands out as being the best in all situations [6]. In a study about the rainfall data in the Indian Himalayas, [PERSON] et al. [7] performed a comparison of several interpolation methods, among which there were several variants of multivariate Kriging and IDW. It was shown that none of the methods performs best in all the studied cases. Principal Component Regression with the Residual correction (PCRR) method, IDW and the Multiple Linear Regression (MLR) methods were used to compare interpolated annual, daily and hourly precipitation and the spatial distribution of precipitation in the Xinxie catchment [8], while the spatial distribution of precipitation deficit over Seyhan River basin using IDW is reported in [9]. The basic IDW method is successfully employed in [10] to estimate the rainfall distribution in a region of Iraq. The scenario uses incremental values for the power parameter, in the range from 1 to 5. A modified IDW method is investigated in [11], where the elevation is also considered for estimating the values at unknown locations. A new method for estimating the regional precipitation (MPPM) has been introduced in [12] and its performance has been tested against that of IDW and kriging. It was reported that MPPM avoids the problems that could appear in the application of kriging methods, as (i) the invertibility of the distance matrix, (ii) the high computational cost related to building the inverse of the distance matrix in the case of a high number of stations, (iii) the choice of the model for the estimation of theoretical variogram and (iv) the selection of the optimal parameters of the variogram model. It was shown that deterministic methods could be good competitors for spatial interpolation techniques, such as KG, performing better than the last ones in the study cases presented in [12]. In an engineering setting, [PERSON] et al. [13] investigate a hybridization of IDW with a harmony search. The obtained metamodel significantly reduces the computational effort and improves the convergence rate.