Computing the Beta Parameter in IDW Interpolation by Using a Genetic Algorithm

This article proposes a new approach for determining the optimal parameter (β) in Inverse Distance Weighted Method (IDW) spatial interpolation of hydrological data series. is based on genetic algorithm (GA) and finds unique β entire study region, while classical one determines different βs interpolated The proposed four scenarios crossover/mutation: single-point/uniform, single-point/swap, two-point/uniform, two-point swap. Its performances are evaluated series collected 41 years at ten observation sites, terms mean absolute error (MAE) standard (MSE). smallest errors obtained swap scenario. Comparisons results with those ordinary kriging (KG), IDW (with = 2 optimum beta found by our algorithm), Optimized Particle Swarm Optimization (OIDW) each show that present better performs 70% (80%) cases.