dc.creator | Kenda K., Peternelj J., Mellios N., Kofinas D., Čerin M., Rožanec J. | en |
dc.date.accessioned | 2023-01-31T08:43:10Z | |
dc.date.available | 2023-01-31T08:43:10Z | |
dc.date.issued | 2020 | |
dc.identifier | 10.2166/aqua.2020.143 | |
dc.identifier.issn | 00037214 | |
dc.identifier.uri | http://hdl.handle.net/11615/74829 | |
dc.description.abstract | The paper presents a thorough evaluation of the performance of different statistical modeling techniques in ground- and surface-level prediction scenarios as well as some aspects of the application of data-driven modeling in practice (feature generation, feature selection, heterogeneous data fusion, hyperparameter tuning, and model evaluation). Twenty-one different regression and classification techniques were tested. The results reveal that batch regression techniques are superior to incremental techniques in terms of accuracy and that among them gradient boosting, random forest and linear regression perform best. On the other hand, introduced incremental models are cheaper to build and update and could still yield good enough results for certain large-scale applications. © 2020 The Authors Journal of Water Supply: Research and Technology-AQUA | en |
dc.language.iso | en | en |
dc.source | Journal of Water Supply: Research and Technology - AQUA | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089568825&doi=10.2166%2faqua.2020.143&partnerID=40&md5=7936e7f836a3d528c815b157def8c3a6 | |
dc.subject | Data fusion | en |
dc.subject | Decision trees | en |
dc.subject | Groundwater | en |
dc.subject | Statistical methods | en |
dc.subject | Classification technique | en |
dc.subject | Feature generation | en |
dc.subject | Heterogeneous data | en |
dc.subject | Incremental models | en |
dc.subject | Incremental techniques | en |
dc.subject | Large-scale applications | en |
dc.subject | Regression techniques | en |
dc.subject | Statistical modeling | en |
dc.subject | Petroleum reservoir evaluation | en |
dc.subject | groundwater | en |
dc.subject | hydrological modeling | en |
dc.subject | linear programing | en |
dc.subject | numerical model | en |
dc.subject | performance assessment | en |
dc.subject | prediction | en |
dc.subject | regression analysis | en |
dc.subject | IWA Publishing | en |
dc.title | Usage of statistical modeling techniques in surface and groundwater level prediction | en |
dc.type | journalArticle | en |