Traditional methods such as distance weighing, correlation and data driven methods have been used in the estimation of missing precipitation data. Also common is the use of radar (NEXRAD) data to provide better spatial distribution of precipitation as well as infilling missing rain gage data. Conventional regression models are often used to capture highly variant nonlinear spatial and temporal relationships between NEXRAD and rain gage data. This study aims to understand and model the relationships between radar (NEXRAD) estimated rainfall data and the data measured by conventional rain gages. The study is also an investigation into the use of emerging computational data modeling (inductive) techniques and mathematical programming formulations to develop new optimal functional approximations. Radar based rainfall data and rain gage data are analyzed to understand the spatio-temporal associations, as well as the effect of changes in the length or availability of data on the models. The upper and lower Kissimmee basins of south Florida form the test-bed to evaluate the proposed and developed approaches and also to check the validity and operational applicability of these functional relationships among NEXRAD and rain gage data for infilling of missing data.