Hybrid interpolation approach for estimating the spatial variation of annual precipitation in the Macta basin, Algeria

Spatial precipitation analysis is essential for effectively managing hydrological modeling, construction of water structures, and irrigation planning. In this study, the ordinary kriging (OK), simple kriging (SK), global polynomial interpolation (GPI), local polynomial interpolation (LPI), inverse distance weighted (IDW), radial basis functions (RBF), and artificial neural network (ANN)-based hybrid techniques were compared to determine the spatial variation of annual precipitation. Statistical indicators derived from Willmott’s index of agreement, root mean square error, mean absolute percentage error, and the violin plot and boxplot graphical approaches were used to determine the most effective technique for precipitation interpolation. According to the analysis results, it has been observed that the ANN model significantly improves the prediction performance of single interpolation methods. The OK-ANN hybrid model was determined to be the most accurate representation of precipitation distribution, with the GPI-ANN model coming in second. The most precise results were obtained using the deterministic method, RBF with inverse multiquadric kernel function, LPI with Epanechnikov kernel function, and GPI with 3rd-order polynomial interpolations. In addition, it was determined that deterministic approaches produce more successful results than geostatistical approaches in the basin due to the presence of homogeneous and densely distributed meteorological observation networks.