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• W2014166182 abstract "Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize hydrosystem benefits. In this study, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river–reservoir systems. In a case study of the Lower Colorado River system in central Texas, a number of potential predictors are evaluated for forecasting seasonal streamflow, including large-scale climate indices related to the El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and others. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas." @default.
• W2014166182 created "2016-06-24" @default.
• W2014166182 creator A5021369812 @default.
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• W2014166182 date "2011-11-01" @default.
• W2014166182 modified "2023-09-25" @default.
• W2014166182 title "Data mining methods for hydroclimatic forecasting" @default.
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• W2014166182 doi "https://doi.org/10.1016/j.advwatres.2011.08.001" @default.
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