Machine Learning Enhanced by Feature Engineering for Estimating Snow Water Equivalent

Machine Learning Enhanced by Feature Engineering for Estimating Snow Water Equivalent

This study compares the calculation of snow water equivalent (SWE) using machine learning algorithms with the conventional degree-day method. The study uses machine learning techniques such as LASSO, Random Forest, Support Vector Machines, and CatBoost. It proposes an innovative use of feature engin...

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Bibliographic Details
Published in:Water (Basel) Vol. 16; no. 16; p. 2285
Main Authors: Čistý, Milan, Danko, Michal, Kohnová, Silvia, Považanová, Barbora, Trizna, Andrej
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-08-2024
Subjects:
Accuracy
Algorithms
Decision making
Decision trees
degree-day method
Energy
feature engineering
Hydrology
Machine learning
Methods
Missing data
Neural networks
Precipitation
Radiation
snow water equivalent
Temperature
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Summary:This study compares the calculation of snow water equivalent (SWE) using machine learning algorithms with the conventional degree-day method. The study uses machine learning techniques such as LASSO, Random Forest, Support Vector Machines, and CatBoost. It proposes an innovative use of feature engineering (FE) to improve the accuracy and robustness of SWE predictions by machine learning intended for interpolation, extrapolation, or imputation of missing data. The performance of machine learning approaches is evaluated against the traditional degree-day method for predicting SWE. The study emphasizes and demonstrates gains when modeling is enhanced by transforming basic, raw data through feature engineering. The results, verified in a case study from the mountainous region of Slovakia, suggest that machine learning, particularly CatBoost with feature engineering, shows better results in SWE estimation in comparison with the degree-day method, although the authors present a refined application of the degree-day method by utilizing genetic algorithms. Nevertheless, the study finds that the degree-day method achieved accuracy with a Nash–Sutcliffe coefficient of efficiency NSE = 0.59, while the CatBoost technique enhanced with the proposed FE achieved an accuracy NSE = 0.86. The results of this research contribute to refining snow hydrology modeling and optimizing SWE prediction for improved decision-making in snow-dominated regions.
ISSN:2073-4441
2073-4441
DOI:10.3390/w16162285