Dr. Alina Bărbulescu
Associate Professor, Doctoral School of Technical University of Civil Engineering, Bucharest, Romania and Ovidius University of Constanta, Romania
Alina Bărbulescu graduated from the University of Craiova, Romania, Faculty of Mathematics and from Petre Andrei University of Iasi, Romania (Faculty of Law). After a Master in Mathematics at Bucharest University (Romania), she got the Ph.D in Mathematics, from Al I Cuza University of Iasi (Romania), the Ph.D in Cybernetics and Economic Statistics, from Academy of Economic Studies Bucharest (Romania) and the Ph.D in Civil Engineering, with Magna cum Laude, at the Technical University of Civil Engineering, Bucharest (Romania). She got the habilitation in Civil Engineering in 2014, with the Thesis Modeling the spatial distributed precipitations. Dr. Barbulescu works in the field of mathematics, applied statistics and hydrological modelling. Nowadays she works at Ovidius University of Constanta, Faculty of Mathematics and Computer Science and is Ph.D supervisor at Technical University of Civil Engineering Bucharest, Romania. She is author of 23 books and over 150 articles, published in peer rewieved international journals, invited editor for 16 books and special issues of international journals, being also a member of editorial boards of 6 journals, among which International Journal of Mathematics and Computation and International Journal of Applied Mathematics and Statistics.

Speech Title: Estimating the Regional Precipitation

Abstract: Rainfall estimating is important for flood forecasting, water balance computation, water resources and drought management. Estimating, modeling and predicting the precipitation evolution are of practical importance, especially in arid regions. Geostatistics offers a range of methods to estimate the precipitation at regional scale, but sometimes these methods require additional data that are not always available or very complicated algorithms. In this context, we present new methods for estimating the regional precipitations that are easy to implement and use. They extend the Most Probable Precipitation Method (MPPM), are based on matrix computation and Swarm Particle Optimization. Better results are obtained compared with the ordinary kriging, IDW and Thiessen polygons method.