The paper presents a thorough evaluation of the performance of different statistical modeling techniques in ground- and surface-level prediction scenarios (such as Water4Cities and NAIADES projects) 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.

The Journal of Water Supply: Research and Technology – AQUA. publishes peer-reviewed scientific and technical, review, and practical papers dealing with research and development in water supply technology and management covering the complete water cycle.

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