Comparison of Machine Learning Techniques Powering Flood Early Warning Systems. Application to a catchment located in the Tropical Andes of Ecuador.

Abstract:

Flood Early Warning Systems have globally become an effective tool to mitigate the adverse effects of this natural hazard on society, economy and environment. A novel approach for such systems is to actually forecast flood events rather than merely monitoring the catchment hydrograph evolution on its way to an inundation site. A wide variety of modelling approaches, from fully-physical to data-driven, have been developed depending on the availability of information describing intrinsic catchment characteristics. However, during last decades, the use of Machine Learning techniques has remarkably gained popularity due to its power to forecast floods at a minimum of demanded data and computational cost. Here, we selected the algorithms most commonly employed for flood pbkp_rediction (K-nearest Neighbors, Logistic Regression, Random Forest, Naïve Bayes and Neural Networks), and used them in a precipitation …

Año de publicación:

2020

Keywords:

Fuente:

google