34
P. Muñoz et al.
Fernández de Córdova Webster C, Javier Rodríguez López Y (2016) Primeros resultados de la
red actual de monitoreohidrometeorológico de Cuenca, Ecuador. Ing. Hidráulica y Ambient 37:
44–56
Galelli S, Castelletti A (2013) Assessing the predictive capability of randomized tree-based
ensembles in streamflow modelling. Hydrol Earth Syst Sci 17:2669–2684
Gupta HV, Wagener T, Liu Y, (2008). Reconciling theory with observations: elements of a diagnostic
approach to model evaluation. Hydrol Process https://doi.org/10.1002/hyp.6989
Jin L, Kuang X, Huang H, Qin Z, Wang Y (2005) Study on the overfitting of the artificial neural
network forecasting model. Acta Meteorol Sin 19:216–225
Kim S, Matsumi Y, Pan S, Mase H (2016) A real-time forecast model using artificial neural network
for after-runner storm surges on the Tottori coast. Jpn Ocean Eng 122:44–53. https://doi.org/10.
1016/j.oceaneng.2016.06.017
Kubal C, Haase D, Meyer V, Scheuer S (2009) Integrated urban flood risk assessment-adapting a
multicriteria approach to a city. Nat Hazards Earth Syst Sci 9:1881
Kühnlein M, Appelhans T, Thies B, Nauss T (2014) Improving the accuracy of rainfall rates from
optical satellite sensors with machine learning—A random forests-based approach applied to
MSG SEVIRI. Remote Sens Environ 141:129–143. https://doi.org/10.1016/j.rse.2013.10.026
Martens D, De Backer M, Haesen R, Vanthienen J, Snoeck M, Baesens B (2007) Classification
with ant colony optimization. IEEE Trans Evol Comput 11:651–665
Min SK, Zhang X, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation
extremes. Nature 470:378–381. https://doi.org/10.1038/nature09763
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation
guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE
50:885–900
Mosavi A, Ozturk P, Chau KW (2018) Flood prediction using machine learning models: literature
review. Water (Switzerland) 10:1–40. https://doi.org/10.3390/w10111536
Muñoz P, Orellana-Alvear J, Willems P, Célleri R (2018). Flash-flood forecasting in an andean mountain catchment-development of a step-wise methodology based on the random forest algorithm.
Water (Switzerland) 10. https://doi.org/10.3390/w10111519
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P,
Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay
E (2011) Scikit-learn: machine learning in {P}ython. J Mach Learn Res 12:2825–2830
Peleg N, Gvirtzman H (2010) Groundwater flow modeling of two-levels perched karstic leaking
aquifers as a tool for estimating recharge and hydraulic parameters. J Hydrol 388:13–27
Probst P, Wright M, Boulesteix A-L (2018). Hyperparameters and tuning strategies for random
forest. 1–18
Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, Chica-Olmo M, Ribeiro L (2014) Predictive
modeling of groundwater nitrate pollution using random forest and multisource variables related
to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain). Sci
Total Environ 476:189–206
Ruin I, Creutin JD, Anquetin S, Lutoff C (2008) Human exposure to flash floods—relation between
flood parameters and human vulnerability during a storm of September 2002 in Southern France.
J Hydrol 361:199–213. https://doi.org/10.1016/j.jhydrol.2008.07.044
Sofia G, Roder G, Dalla Fontana G, Tarolli P (2017) Flood dynamics in urbanised landscapes: 100
years of climate and humans’ interaction. Sci Rep 7:1–12. https://doi.org/10.1038/srep40527
Sudheer KP, Gosain AK, Ramasastri KS (2002) A data-driven algorithm for constructing artificial
neural network rainfall-runoff models. Hydrol Process 16:1325–1330. https://doi.org/10.1002/
hyp.554
Tang Y, Reed P, Van Werkhoven K, Wagener T (2007) Advancing the identification and evaluation
of distributed rainfall & hyphen runoff models using global sensitivity analysis. 43: 1–14. https://
doi.org/10.1029/2006WR005813
United Nations Office for Disaster Risk Reduction (UNISDR) (2015) Sendai framework for disaster
risk reduction 2015–2030. United Nations Off. Disaster Risk Reduct. 32.
P. Muñoz et al.
Fernández de Córdova Webster C, Javier Rodríguez López Y (2016) Primeros resultados de la
red actual de monitoreohidrometeorológico de Cuenca, Ecuador. Ing. Hidráulica y Ambient 37:
44–56
Galelli S, Castelletti A (2013) Assessing the predictive capability of randomized tree-based
ensembles in streamflow modelling. Hydrol Earth Syst Sci 17:2669–2684
Gupta HV, Wagener T, Liu Y, (2008). Reconciling theory with observations: elements of a diagnostic
approach to model evaluation. Hydrol Process https://doi.org/10.1002/hyp.6989
Jin L, Kuang X, Huang H, Qin Z, Wang Y (2005) Study on the overfitting of the artificial neural
network forecasting model. Acta Meteorol Sin 19:216–225
Kim S, Matsumi Y, Pan S, Mase H (2016) A real-time forecast model using artificial neural network
for after-runner storm surges on the Tottori coast. Jpn Ocean Eng 122:44–53. https://doi.org/10.
1016/j.oceaneng.2016.06.017
Kubal C, Haase D, Meyer V, Scheuer S (2009) Integrated urban flood risk assessment-adapting a
multicriteria approach to a city. Nat Hazards Earth Syst Sci 9:1881
Kühnlein M, Appelhans T, Thies B, Nauss T (2014) Improving the accuracy of rainfall rates from
optical satellite sensors with machine learning—A random forests-based approach applied to
MSG SEVIRI. Remote Sens Environ 141:129–143. https://doi.org/10.1016/j.rse.2013.10.026
Martens D, De Backer M, Haesen R, Vanthienen J, Snoeck M, Baesens B (2007) Classification
with ant colony optimization. IEEE Trans Evol Comput 11:651–665
Min SK, Zhang X, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation
extremes. Nature 470:378–381. https://doi.org/10.1038/nature09763
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation
guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE
50:885–900
Mosavi A, Ozturk P, Chau KW (2018) Flood prediction using machine learning models: literature
review. Water (Switzerland) 10:1–40. https://doi.org/10.3390/w10111536
Muñoz P, Orellana-Alvear J, Willems P, Célleri R (2018). Flash-flood forecasting in an andean mountain catchment-development of a step-wise methodology based on the random forest algorithm.
Water (Switzerland) 10. https://doi.org/10.3390/w10111519
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P,
Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay
E (2011) Scikit-learn: machine learning in {P}ython. J Mach Learn Res 12:2825–2830
Peleg N, Gvirtzman H (2010) Groundwater flow modeling of two-levels perched karstic leaking
aquifers as a tool for estimating recharge and hydraulic parameters. J Hydrol 388:13–27
Probst P, Wright M, Boulesteix A-L (2018). Hyperparameters and tuning strategies for random
forest. 1–18
Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, Chica-Olmo M, Ribeiro L (2014) Predictive
modeling of groundwater nitrate pollution using random forest and multisource variables related
to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain). Sci
Total Environ 476:189–206
Ruin I, Creutin JD, Anquetin S, Lutoff C (2008) Human exposure to flash floods—relation between
flood parameters and human vulnerability during a storm of September 2002 in Southern France.
J Hydrol 361:199–213. https://doi.org/10.1016/j.jhydrol.2008.07.044
Sofia G, Roder G, Dalla Fontana G, Tarolli P (2017) Flood dynamics in urbanised landscapes: 100
years of climate and humans’ interaction. Sci Rep 7:1–12. https://doi.org/10.1038/srep40527
Sudheer KP, Gosain AK, Ramasastri KS (2002) A data-driven algorithm for constructing artificial
neural network rainfall-runoff models. Hydrol Process 16:1325–1330. https://doi.org/10.1002/
hyp.554
Tang Y, Reed P, Van Werkhoven K, Wagener T (2007) Advancing the identification and evaluation
of distributed rainfall & hyphen runoff models using global sensitivity analysis. 43: 1–14. https://
doi.org/10.1029/2006WR005813
United Nations Office for Disaster Risk Reduction (UNISDR) (2015) Sendai framework for disaster
risk reduction 2015–2030. United Nations Off. Disaster Risk Reduct. 32.