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Article

KNN vs. Bluecat—Machine Learning vs. Classical Statistics

1
Institute for Environmental Research & Sustainable Development, National Observatory of Athens, 15236 Athens, Greece
2
Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, 15780 Athens, Greece
3
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Xiaodong Chen
Hydrology 2022, 9(6), 101; https://doi.org/10.3390/hydrology9060101
Received: 16 May 2022 / Revised: 28 May 2022 / Accepted: 31 May 2022 / Published: 6 June 2022
Uncertainty is inherent in the modelling of any physical processes. Regarding hydrological modelling, the uncertainty has multiple sources including the measurement errors of the stresses (the model inputs), the measurement errors of the hydrological process of interest (the observations against which the model is calibrated), the model limitations, etc. The typical techniques to assess this uncertainty (e.g., Monte Carlo simulation) are computationally expensive and require specific preparations for each individual application (e.g., selection of appropriate probability distribution). Recently, data-driven methods have been suggested that attempt to estimate the uncertainty of a model simulation based exclusively on the available data. In this study, two data-driven methods were employed, one based on machine learning techniques, and one based on statistical approaches. These methods were tested in two real-world case studies to obtain conclusions regarding their reliability. Furthermore, the flexibility of the machine learning method allowed assessing more complex sampling schemes for the data-driven estimation of the uncertainty. The anatomisation of the algorithmic background of the two methods revealed similarities between them, with the background of the statistical method being more theoretically robust. Nevertheless, the results from the case studies indicated that both methods perform equivalently well. For this reason, data-driven methods can become a valuable tool for practitioners. View Full-Text
Keywords: k-nearest neighbours; data-driven modelling; model uncertainty; machine learning; statistical analysis; hydrological modelling k-nearest neighbours; data-driven modelling; model uncertainty; machine learning; statistical analysis; hydrological modelling
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MDPI and ACS Style

Rozos, E.; Koutsoyiannis, D.; Montanari, A. KNN vs. Bluecat—Machine Learning vs. Classical Statistics. Hydrology 2022, 9, 101. https://doi.org/10.3390/hydrology9060101

AMA Style

Rozos E, Koutsoyiannis D, Montanari A. KNN vs. Bluecat—Machine Learning vs. Classical Statistics. Hydrology. 2022; 9(6):101. https://doi.org/10.3390/hydrology9060101

Chicago/Turabian Style

Rozos, Evangelos, Demetris Koutsoyiannis, and Alberto Montanari. 2022. "KNN vs. Bluecat—Machine Learning vs. Classical Statistics" Hydrology 9, no. 6: 101. https://doi.org/10.3390/hydrology9060101

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