| Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation H Foroozand, SV Weijs Entropy 19 (10), 520, 2017 | 13 | 2017 |
| Dependency and Redundancy: How Information Theory Untangles Three Variable Interactions in Environmental Data SV Weijs, H Foroozand, A Kumar Water Resources Research 54 (10), 7143-7148, 2018 | 11 | 2018 |
| Application of entropy ensemble filter in neural network forecasts of tropical Pacific sea surface temperatures H Foroozand, V Radić, SV Weijs Entropy 20 (3), 207, 2018 | 9 | 2018 |
| Objective functions for information-theoretical monitoring network design: what is “optimal”? H Foroozand, SV Weijs Hydrology and Earth System Sciences 25 (2), 831-850, 2021 | 8 | 2021 |
| A comparative study of honey-bee mating optimization algorithm and support vector regression system approach for river discharge prediction Case study: Kashkan River Basin H Foroozand, SH Afzali International Conference on Civil Engineering Architecture and urban …, 2015 | 4 | 2015 |
| Application of machine learning and information theory to monitor and predict environmental signals H Foroozand UNIVERSITY OF BRITISH COLUMBIA (Vancouver, 2021 | | 2021 |
| Tracking bits of information through forecasting systems: from source to decision S Weijs, H Foroozand EGU General Assembly Conference Abstracts, 13223, 2020 | | 2020 |
| Entropy Ensemble Filter: Does information content assessment of bootstrapped training datasets before model training lead to better trade-off between ensemble size and … H Foroozand, SV Weijs EGU General Assembly Conference Abstracts, 1963, 2020 | | 2020 |
| Information theory-based location of sensor nodes: what is optimal? SV Weijs, H Foroozand AGU Fall Meeting Abstracts 2018, H11M-1629, 2018 | | 2018 |
| SOURCING AND CHANNELLING INFORMATION FLOWS FOR HYDROLOGICAL PREDICTION SV Weijs, H Foroozand, A Kumar, LC Galindo | | 2017 |
steven v. weijsUniversity of British Columbia在 civil.ubc.ca 的电子邮件经过验证
