Usage of statistical modeling techniques in surface and groundwater level prediction

Klemen Kenda, Jože Peternelj, Nikos Mellios, Dimitris Kofinas, Matej Čerin, Jože Rožanec
2020 Journal of Water Supply: Research and Technology - Aqua  
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
more » ... l 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.
doi:10.2166/aqua.2020.143 fatcat:szdyjus6czfh5e55qzzpyx2ium