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Comparison of Machine Learning Techniques Powering Flood Early Warning Systems
OtherAbstract: Hydrological extremes (especially floods) have multiple impacts on society. Flood frequency and sevePalabras claves:Autores:Johanna Orellana-Alvear, Rolando Enrique Célleri AlvearFuentes:googleComparison 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:googleAssessment of native radar reflectivity and radar rainfall estimates for discharge forecasting in mountain catchments with a random forest model
ArticleAbstract: Discharge forecasting is a key component for early warning systems and extremely useful for decisionPalabras claves:Andes, Discharge forecasting, Machine learning, Mountain region, Native radar data, Radar rainfall, Radar reflectivity, X-bandAutores:Bendix J., Contreras P., Johanna Orellana-Alvear, Paul Muñoz, Rolando Enrique Célleri Alvear, Rollenbeck R.Fuentes:googlescopusDetermination of climatic conditions related to precipitation anomalies in the Tropical Andes by means of the random forest algorithm and novel climate indices
ArticleAbstract: Understanding precipitation and its relation with atmospheric and oceanic conditions is vital in thePalabras claves:Andes, climate anomalies, K-Means, large-scale climate oscillations, Machine learning, rainfall, SOUTH AMERICA, tropicsAutores:Johanna Orellana-Alvear, Mario Guallpa, Rolando Enrique Célleri Alvear, Rollenbeck R.Fuentes:googlescopusCalibration of X-band radar for extreme events in a spatially complex precipitation region in north peru: Machine learning vs. empirical approach
ArticleAbstract: Cost-efficient single-polarized X-band radars are a feasible alternative due to their high sensitiviPalabras claves:extreme events, Machine learning, Quantitative precipitation estimate, random forest, Tropical desert, Tropical mountains, weather radarAutores:Johanna Orellana-Alvear, Macalupu S., Nolasco P., Rodriguez R., Rollenbeck R.Fuentes:googlescopusApplication of LS-SVMs to ozone forecasting in Belgium
OtherAbstract: Very few circumstances in my life have made me feel so challenged. This experience studying abroad hPalabras claves:Autores:Johanna Orellana-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:googleExploring a machine learning model as radar rainfall retrieval of the highest X-band radar in the world.
OtherAbstract: Quantitative precipitation estimation (QPE) from weather radar data is crucial for hydrological applPalabras claves:Autores:Johanna Orellana-Alvear, Rolando Enrique Célleri 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:googlescopus