Dynamic data-driven genetic algorithm for forest fire spread prediction M Denham, K Wendt, G Bianchini, A Cortés, T Margalef Journal of Computational Science 3 (5), 398-404, 2012 | 73 | 2012 |
Wildland fire growth prediction method based on multiple overlapping solution G Bianchini, M Denham, A Cortés, T Margalef, E Luque Journal of Computational Science 1 (4), 229-237, 2010 | 65 | 2010 |
Evolutionary-Statistical System: A parallel method for improving forest fire spread prediction G Bianchini, P Caymes-Scutari, M Méndez-Garabetti Journal of Computational Science 6, 58-66, 2015 | 42 | 2015 |
Increase in the quality of the prediction of a computational wildfire behavior method through the improvement of the internal metaheuristic M Méndez-Garabetti, G Bianchini, P Caymes-Scutari, ML Tardivo Fire Safety Journal 82, 49-62, 2016 | 27 | 2016 |
Improved prediction methods for wildfires using high performance computing: A comparison G Bianchini, A Cortés, T Margalef, E Luque International Conference on Computational Science, 539-546, 2006 | 26 | 2006 |
Wildland fire prediction based on statistical analysis of multiple solutions G Bianchini Universitat Autònoma de Barcelona,, 2006 | 25 | 2006 |
S2F2M – Statistical System for Forest Fire Management G Bianchini, A Cortés, T Margalef, E Luque International Conference on Computational Science, 427-434, 2005 | 25 | 2005 |
A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction ML Tardivo, P Caymes-Scutari, G Bianchini, M Méndez-Garabetti, ... Procedia Computer Science 108, 2018-2027, 2017 | 19 | 2017 |
Comparative analysis of performance and quality of prediction between ESS and ESS-IM M Méndez-Garabetti, G Bianchini, ML Tardivo, P Caymes-Scutari Electronic Notes in Theoretical Computer Science 314, 45-60, 2015 | 19 | 2015 |
Improving Forest-Fire Prediction by Applying Statistical Approach G Bianchini, M Denham, A Cortés, T Margalef, E Luque V International Conference on Forest Fire Research, 1-14, 2006 | 17 | 2006 |
Between classical and ideal: enhancing wildland fire prediction using cluster computing B Abdalhaq, A Cortés, T Margalef, G Bianchini, E Luque Cluster Computing 9, 329-343, 2006 | 16 | 2006 |
Estimation of volume rendering efficiency with gpu in a parallel distributed environment CFP Monte, F Piccoli, C Luciano, S Rizzi, G Bianchini, PC Scutari Procedia Computer Science 18, 1402-1411, 2013 | 13 | 2013 |
Environment for automatic development and tuning of parallel applications P Caymes-Scutari, G Bianchini, A Sikora, T Margalef 2016 International Conference on High Performance Computing & Simulation …, 2016 | 12 | 2016 |
Uncertainty reduction method based on statistics and parallel evolutionary algorithms G Bianchini, P Caymes-Scutari IV High-Performance Computing Symposium (HPC 2011)(XL JAIIO, Córdoba, 31 de …, 2011 | 11 | 2011 |
Hierarchical parallel model for improving performance on differential evolution ML Tardivo, P Caymes‐Scutari, G Bianchini, M Méndez‐Garabetti Concurrency and Computation: Practice and Experience 29 (10), e4087, 2017 | 10 | 2017 |
Optimization for an uncertainty reduction method applied to forest fires spread prediction ML Tardivo, P Caymes-Scutari, M Méndez-Garabetti, G Bianchini Computer Science–CACIC 2017: 23rd Argentine Congress, La Plata, Argentina …, 2018 | 9 | 2018 |
Evolutionary-statistical system for uncertainty reduction problems in wildfires G Bianchini, M Méndez Garabetti, P Caymes Scutari XVIII Congreso Argentino de Ciencias de la Computación, 2012 | 9 | 2012 |
Wildland fire risk maps using S2F2M* G Bianchini, A Cortés, T Margalef, EL Fadón, E Chuvieco, A Camia Journal of Computer Science and Technology 5 (04), 244-249, 2005 | 9 | 2005 |
Two models for parallel differential evolution ML Tardivo, P Caymes-Scutari, M Mendez-Garabetti, G Bianchini Proceedings of HPCLatAm, 25-36, 2013 | 7 | 2013 |
Hybrid-parallel uncertainty reduction method applied to forest fire spread prediction M Mendez Garabetti, G Bianchini, ML Tardivo, PG Caymes Scutari, ... Ibero-American Science and Technology Education Consortium, 2017 | 6 | 2017 |