A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning-Reference-Cited by-同舟云学术

A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning

Published:2024-09-04 Issue: Volume:15 Page:60
ISSN:
Container-title:The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024)
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Author:

Zanutto Dennis¹²^ORCID,Michalopoulos Christos¹³^ORCID,Chatzistefanou Georgios-Alexandros¹⁴,Vamvakeridou-Lyroudia Lydia¹⁴,Tsiami Lydia¹³^ORCID,Glynis Konstantinos¹⁵,Samartzis Panagiotis⁶,Hermes Luca⁷,Hinder Fabian⁷,Vaquet Jonas⁷,Vaquet Valerie⁷⁸^ORCID,Eliades Demetrios⁸^ORCID,Polycarpou Marios⁸,Koundouri Phoebe⁹¹⁰¹¹¹²¹³^ORCID,Hammer Barbara⁷^ORCID,Savić Dragan¹⁴^ORCID

Affiliation:

1. KWR Water Research Institute, 3433 PE Nieuwegein, The Netherlands

2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy

3. Department of Water Resources and Environmental Engineering, National Technical University of Athens, 15773 Athens, Greece

4. Centre for Water Systems, University of Exeter, Exeter EX4 4QD, UK

5. Department of Water Management, TU Delft, 2628 CN Delft, The Netherlands

6. Department of Economics, University of Macedonia, 54636 Thessaloniki, Greece

7. Machine Learning Group, CITEC, Bielefeld University, 33619 Bielefeld, Germany

8. KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia 2109, Cyprus

9. Department of International & European Economic Studies, Athens University of Economics and Business, 10434 Athens, Greece

10. Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kongens Lyngby, Denmark

11. ATHENA Research Center, 26504 Patras, Greece

12. UN SDSN Global Climate Hub, 10434 Athens, Greece

13. UN SDSN Europe, 75009 Paris, France

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/69/1/60/pdf

Reference9 articles.

1. House-Peters, L.A., and Chang, H. (2011). Urban Water Demand Modeling: Review of Concepts, Methods, and Organizing Principles. Water Resour. Res., 47.

2. Zanutto, D., Michalopoulos, C., Samartzis, P., Hermes, L., Vaquet, J., Vaquet, V., and Eliades, D. (2024). WaterFutures/BoN2024: BWDF Code, Models and Dashboard, Version v5.0.0, Zenodo.

3. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4–9). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA.

4. Chen, T., and Guestrin, C. (2016, January 13–17). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.

5. van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K. (2016). WaveNet: A Generative Model for Raw Audio 2016. arXiv.