| Data‐driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks Z Guo, JP Leitao, NE Simões, V Moosavi Journal of Flood Risk Management 14 (1), e12684, 2021 | 90 | 2021 |
| Machine learning assisted evaluations in structural design and construction H Zheng, V Moosavi, M Akbarzadeh Automation in Construction 119, 103346, 2020 | 51 | 2020 |
| Urban morphology meets deep learning: Exploring urban forms in one million cities, towns, and villages across the planet V Moosavi Machine learning and the city: Applications in architecture and urban design …, 2022 | 48 | 2022 |
| Data-driven rapid flood prediction mapping with catchment generalizability Z Guo, V Moosavi, JP Leitão Journal of Hydrology 609, 127726, 2022 | 31 | 2022 |
| Data-driven design: Exploring new structural forms using machine learning and graphic statics L Fuhrimann, V Moosavi, PO Ohlbrock, P D’acunto Proceedings of IASS Annual Symposia 2018 (2), 1-8, 2018 | 31 | 2018 |
| Modeling urban traffic dynamics in coexistence with urban data streams V Moosavi, L Hovestadt Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, 1-7, 2013 | 31 | 2013 |
| Optimising the load path of compression-only thrust networks through independent sets A Liew, R Avelino, V Moosavi, T Van Mele, P Block Structural and Multidisciplinary Optimization 60, 231-244, 2019 | 28 | 2019 |
| Beyond typologies, beyond optimization: Exploring novel structural forms at the interface of human and machine intelligence K Saldana Ochoa, PO Ohlbrock, P D’Acunto, V Moosavi International Journal of Architectural Computing 19 (3), 466-490, 2021 | 25 | 2021 |
| SOMPY: A Python Library for Self Organizing Map (SOM) V Moosavi, S Packmann, I Vallés https://github.com/sevamoo/SOMPY, 2019 | 21* | 2019 |
| Contextual mapping: Visualization of high-dimensional spatial patterns in a single geo-map V Moosavi Computers, Environment and Urban Systems 61, 1-12, 2017 | 19 | 2017 |
| A Markovian model of evolving world input-output network V Moosavi, G Isacchini PloS one 12 (10), e0186746, 2017 | 17 | 2017 |
| A fuzzy reinforcement learning algorithm for inventory control in supply chains MHF Zarandi, SV Moosavi, M Zarinbal The International Journal of Advanced Manufacturing Technology 65, 557-569, 2013 | 16 | 2013 |
| Finding candidate locations for aerosol pollution monitoring at street level using a data-driven methodology V Moosavi, G Aschwanden, E Velasco Atmospheric Measurement Techniques 8 (9), 3563-3575, 2015 | 15 | 2015 |
| Computing with contextual numbers V Moosavi arXiv preprint arXiv:1408.0889, 2014 | 14 | 2014 |
| Sompy: A python library for self organizing map (som). GitHub V Moosavi, S Packmann, I Vallés | 13 | 2014 |
| Datadriven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks, J. Flood Risk Manage., 14, e12684 Z Guo, JP Leitao, NE Simões, V Moosavi | 12 | 2021 |
| Application of Self Organizing Map (SOM) to model a machining process M Saraee, SV Moosavi, S Rezapour Journal of Manufacturing Technology Management, 2011 | 12 | 2011 |
| Urban data streams and machine learning: a case of swiss real estate market V Moosavi arXiv preprint arXiv:1704.04979, 2017 | 10 | 2017 |
| Holistic design explorations of building envelopes supported by machine learning F Bertagna, P D'Acunto, PO Ohlbrock, V Moosavi Journal of Facade Design and Engineering 9 (1), 31-46, 2021 | 9 | 2021 |
| Accumulation in coastal West Antarctic ice core records and the role of cyclone activity JS Hosking, R Fogt, ER Thomas, V Moosavi, T Phillips, J Coggins, ... Geophysical Research Letters 44 (17), 9084-9092, 2017 | 9 | 2017 |
João P. LeitãoProf. (ETH Zurich). Senior researcher/ Group leader (Eawag)Verified email at eawag.ch
