Rainfall Estimation from Sparse Data with Fuzzy B-Splines

This paper reports the results obtained in rainfall estimation using Fuzzy B-Splines (FBS). These parametric surfaces generalize the Interval B-Splines (IBS) and combine the modeling flexibility of B-splines with the expressive power of fuzzy techniques (Anile et al. 1995). Starting from a collection of irregularly scattered rainfall data, the proposed technique provides for every point in the domain under investigation a fuzzy value, i.e., a collection of nested intervals indexed by presumption levels α in [0.1]. Given a presumption level the corresponding interval provides the corresponding estimate range for the real rainfall value. The experimental results show that this technique can efficiently provide an approximate model of the rainfall distribution even starting from a small data collection. The accuracy of the model increases with the size of the available data set. The proposed technique allows real-time refinement of the estimates as soon as new data become available: this property makes fuzzy B-Splines a potentially useful monitoring tool in emergencies.