ID:
publications-2436
Type:
Peer reviewed articles
Year:
2020
Authors:
Klemen Kenda; Jože Peternelj; Nikos Mellios; Dimitris Kofinas; Matej Čerin; Jože M. Rožanec
Title:
Usage of statistical modeling techniques in surface and groundwater level prediction
Venue/Journal:
Journal of Water Supply: Research and Technology
DOI:
10.2166/aqua.2020.143
Research type:
Data Management & Analytics
Water System:
Natural Water Bodies
Technical Focus:
Abstract:
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.
Link with Projects:
820985
Link with Tools:
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