Optimal function forms for spatial interpolation of precipitation data

Traditional inverse distance, exponential weighting functions, regression models, and stochastic interpolation techniques are generally used for spatial interpolation and estimation of missing precipitation data. Recently correlation and nearest neighbor based methods were proposed for estimation of missing precipitation data. These methods use distance, variance, correlation coefficients, pattern set in rainfall time series as measures of strength of correlation between observed precipitation measurements. These methods suffer from several limitations due to the mathematical functions or surrogate measures that may not always explain and model the strength of correlation. Use of the fixed functional set genetic algorithm (FFSGA) is proposed and is investigated in the current study to obtain functional forms for spatial interpolation of precipitation data. The functional forms obtained from this method are used for estimation of missing precipitation data at a rainfall gaging station based on data recorded at all other available gaging stations. Historical daily precipitation data obtained from 15 rain gaging stations from the state of Kentucky, USA, is used to test the functional forms and derive conclusions about the efficacy of the proposed FFGSA method in estimation of missing precipitation data.