| Robustness of LSTM neural networks for multi-step forecasting of chaotic time series M Sangiorgio, F Dercole Chaos, Solitons & Fractals 139, 110045, 2020 | 172 | 2020 |
| Potential for sustainable irrigation expansion in a 3° C warmer climate L Rosa, DD Chiarelli, M Sangiorgio, AA Beltran-Peña, MC Rulli, ... Proceedings of the National Academy of Sciences 117 (47), 29526-29534, 2020 | 152 | 2020 |
| Multi-Step Solar Irradiance Forecasting and Domain Adaptation of Deep Neural Networks G Guariso, G Nunnari, M Sangiorgio Energies 13 (15), 3987, 2020 | 41 | 2020 |
| NN-based implicit stochastic optimization of multi-reservoir systems management M Sangiorgio, G Guariso Water 10 (3), 303, 2018 | 39 | 2018 |
| Forecasting of noisy chaotic systems with deep neural networks M Sangiorgio, F Dercole, G Guariso Chaos, Solitons & Fractals 153, 111570, 2021 | 36 | 2021 |
| Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach G Guariso, M Sangiorgio Information 11 (12), 587, 2020 | 33 | 2020 |
| Improved extreme rainfall events forecasting using neural networks and water vapor measures M Sangiorgio, S Barindelli, R Biondi, E Solazzo, E Realini, G Venuti, ... International Conference on Time Series and Forecasting-Proceedings of …, 2019 | 29 | 2019 |
| An empirical assessment of the universality of ANNs to predict oscillatory time series F Dercole, M Sangiorgio, Y Schmirander IFAC-PapersOnLine 53 (2), 1255-1260, 2020 | 17 | 2020 |
| Multi-objective planning of building stock renovation G Guariso, M Sangiorgio Energy Policy 130, 101-110, 2019 | 17 | 2019 |
| Deep learning in multi-step forecasting of chaotic dynamics M Sangiorgio Special Topics in Information Technology, 3-14, 2022 | 15 | 2022 |
| Performance of Implicit Stochastic Approaches to the Synthesis of Multireservoir Operating Rules G Guariso, M Sangiorgio Journal of Water Resources Planning and Management 146 (6), 04020034, 2020 | 15 | 2020 |
| A comparative study on machine learning techniques for intense convective rainfall events forecasting M Sangiorgio, S Barindelli, R Biondi, E Solazzo, E Realini, G Venuti, ... Theory and Applications of Time Series Analysis, 2020 | 15 | 2020 |
| Spatio-Temporal Analysis of Intense Convective Storms Tracks in a Densely Urbanized Italian Basin M Sangiorgio, S Barindelli ISPRS International Journal of Geo-Information 9 (3), 183, 2020 | 14 | 2020 |
| Reconstruction of long-distance bird migration routes using advanced machine learning techniques on geolocator data M Pancerasa, M Sangiorgio, R Ambrosini, N Saino, DW Winkler, ... Journal of the Royal Society Interface 16 (155), 20190031, 2019 | 12 | 2019 |
| Deep Learning in Multi-step Prediction of Chaotic Dynamics: From Deterministic Models to Real-World Systems M Sangiorgio, F Dercole, G Guariso Springer Nature, 2021 | 8 | 2021 |
| Integrating Economy, Energy, Air Pollution in Building Renovation Plans G Guariso, M Sangiorgio IFAC-PapersOnLine 51 (5), 102-107, 2018 | 7 | 2018 |
| A neural approach for multi-reservoir system operation M Sangiorgio Politecnico di Milano, 2016 | 5 | 2016 |
| Neural approaches for time series forecasting M Sangiorgio, F Dercole, G Guariso Deep Learning in Multi-step Prediction of Chaotic Dynamics: From …, 2022 | 4 | 2022 |
| Reconstructing environmental variables with missing field data via end-to-end machine learning M Sangiorgio, S Barindelli, V Guglieri, G Venuti, G Guariso International Conference on Engineering Applications of Neural Networks, 167-178, 2020 | 4 | 2020 |
| Sensitivity of Chaotic Dynamics Prediction to Observation Noise M Sangiorgio, F Dercole, G Guariso IFAC-PapersOnLine 54 (17), 129-134, 2021 | 3 | 2021 |
Giorgio GuarisoProfessor of Environmental Modelling, Politecnico di Milano, Italy在 polimi.it 的电子邮件经过验证
