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Comparison of Machine Learning Techniques Powering Flood Early Warning Systems. Application to a catchment located in the Tropical Andes of Ecuador.
OtherAbstract: Flood Early Warning Systems have globally become an effective tool to mitigate the adverse effects oPalabras claves:Autores:Johanna Orellana-Alvear, Rolando Enrique Célleri AlvearFuentes:googleApplication of a machine learning technique for developing short-term flood and drought forecasting models in tropical mountainous catchments
OtherAbstract: Floods and droughts are among the most common natural hazards worldwide. They produce major impactsPalabras claves:Autores:Johanna Orellana-Alvear, Rolando Enrique Célleri AlvearFuentes:googleExploitation of X-band Weather Radar Data in the Andes High Mountains and Its Application in Hydrology: a Machine Learning Approach
OtherAbstract:Palabras claves:Autores:Johanna Orellana-AlvearFuentes:googleFlash-flood forecasting in an andean mountain catchment-development of a step-wise methodology based on the random forest algorithm
ArticleAbstract: Flash-flood forecasting has emerged worldwide due to the catastrophic socio-economic impacts this haPalabras claves:Flash-flood, forecasting, Lag analysis, Machine learning, Precipitation-runoff, random forestAutores:Johanna Orellana-Alvear, Patrick Willems, Paul Muñoz, Rolando Enrique Célleri AlvearFuentes:googlescopusFlood early warning systems using machine learning techniques: The case of the tomebamba catchment at the southern Andes of Ecuador
ArticleAbstract: Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systePalabras claves:Andes, Flood early warning, forecasting, hydrological extremes, Machine learningAutores:Bendix J., Jan Jozef Albert Feyen, Johanna Orellana-Alvear, Paul Muñoz, Rolando Enrique Célleri AlvearFuentes:googlescopusInfluence of random forest hyperparameterization on short-term runoff forecasting in an andean mountain catchment
ArticleAbstract: The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach fPalabras claves:Machine learning, Optimal hyperparameters, random forest, Runoff forecasting, Tropical AndesAutores:Bendix J., Contreras P., Johanna Orellana-Alvear, Paul Muñoz, Rolando Enrique Célleri AlvearFuentes:googlescopus