The Parameter Optimization in the Inverse Distance Method by Genetic Algorithm for Estimating Precipitation

The results show that the variability of the order of distances is small when the topography of rainfall stations is uniform and the efficiency and reliability of hydrologic modeling and hence of general water resource management can be significantly improved by more accurate rainfall data interpolated by the variable-order inverse distance method.

Abstract

The inverse distance method, one of the commonly used methods for analyzing spatial variation of rainfall, is flexible if the order of distances in the method is adjustable. By applying the genetic algorithm (GA), the optimal order of distances can be found to minimize the difference between estimated and measured precipitation data. A case study of the Feitsui reservoir watershed in Taiwan is described in the present paper. The results show that the variability of the order of distances is small when the topography of rainfall stations is uniform. Moreover, when rainfall characteristic is uniform, the horizontal distance between rainfall stations and interpolated locations is the major factor influencing the order of distances. The results also verify that the variable-order inverse distance method is more suitable than the arithmetic average method and the Thiessen Polygons method in describing the spatial variation of rainfall. The efficiency and reliability of hydrologic modeling and hence of general water resource management can be significantly improved by more accurate rainfall data interpolated by the variable-order inverse distance method.