Estimation of Missing Precipitation Data using Soft Computing based Spatial Interpolation Techniques

Deterministic and stochastic weighting methods are the most frequently used methods for estimating missing rainfall values at a gage based on values recorded at all other available recording gages. Traditional spatial interpolation techniques can be integrated with soft computing techniques to improve the estimation of missing precipitation data. Association rule mining based spatial interpolation approach, universal function approximation based kriging, optimal function approximation and clustering methods are developed and investigated in the current study to estimate missing precipitation values at a gaging station. Historical daily precipitation data obtained from 15 rain gauging stations from a temperate climatic region, Kentucky, USA, are used to test this approach and derive conclusions about efficacy of these methods in estimating missing precipitation data. Results suggest that the use of soft computing techniques in conjunction with a spatial interpolation technique can improve the precipitation estimates and help to address few limitations of traditional spatial interpolation techniques.