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Open AccessArticle

Flash-Flood Forecasting in an Andean Mountain Catchment—Development of a Step-Wise Methodology Based on the Random Forest Algorithm

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Departamento de Recursos Hídricos y Ciencias Ambientales, Universidad de Cuenca, Cuenca 010150, Ecuador
2
Department of Civil Engineering, Hydraulics Section, KU Leuven, 3001 Leuven, Belgium
3
Laboratory for Climatology and Remote Sensing, Faculty of Geography, University of Marburg, 35032 Marburg, Germany
4
Facultad de Ingeniería, Universidad de Cuenca, Cuenca 010150, Ecuador
*
Author to whom correspondence should be addressed.
Water 2018, 10(11), 1519; https://doi.org/10.3390/w10111519
Received: 31 August 2018 / Revised: 11 October 2018 / Accepted: 12 October 2018 / Published: 26 October 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
Flash-flood forecasting has emerged worldwide due to the catastrophic socio-economic impacts this hazard might cause and the expected increase of its frequency in the future. In mountain catchments, precipitation-runoff forecasts are limited by the intrinsic complexity of the processes involved, particularly its high rainfall variability. While process-based models are hard to implement, there is a potential to use the random forest algorithm due to its simplicity, robustness and capacity to deal with complex data structures. Here a step-wise methodology is proposed to derive parsimonious models accounting for both hydrological functioning of the catchment (e.g., input data, representation of antecedent moisture conditions) and random forest procedures (e.g., sensitivity analyses, dimension reduction, optimal input composition). The methodology was applied to develop short-term prediction models of varying time duration (4, 8, 12, 18 and 24 h) for a catchment representative of the Ecuadorian Andes. Results show that the derived parsimonious models can reach validation efficiencies (Nash-Sutcliffe coefficient) from 0.761 (4-h) to 0.384 (24-h) for optimal inputs composed only by features accounting for 80% of the model’s outcome variance. Improvement in the prediction of extreme peak flows was demonstrated (extreme value analysis) by including precipitation information in contrast to the use of pure autoregressive models. View Full-Text
Keywords: flash-flood; precipitation-runoff; forecasting; lag analysis; random forest; machine learning flash-flood; precipitation-runoff; forecasting; lag analysis; random forest; machine learning
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Muñoz, P.; Orellana-Alvear, J.; Willems, P.; Célleri, R. Flash-Flood Forecasting in an Andean Mountain Catchment—Development of a Step-Wise Methodology Based on the Random Forest Algorithm. Water 2018, 10, 1519.

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