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Multi-time scale stream flow predictions: The support vector machines approach
TLDR
We present new data-driven models based on Statistical Learning Theory that were used to forecast flows at two time scales: seasonal flow volumes and hourly stream flows. Expand
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Performance evaluation of a water resources system under varying climatic conditions: Reliability, Resilience, Vulnerability and beyond
TLDR
Reliability, Resilience, and Vulnerability metrics measure different aspects of a water resources system performance. Expand
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Soil Moisture Prediction Using Support Vector Machines
ABSTRACT: Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived fromExpand
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Sparse Bayesian learning machine for real‐time management of reservoir releases
[1] Water scarcity and uncertainties in forecasting future water availabilities present serious problems for basin-scale water management. These problems create a need for intelligent predictionExpand
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Multiobjective analysis of chaotic dynamic systems with sparse learning machines
Sparse learning machines provide a viable framework for modeling chaotic time-series systems. A powerful state-space reconstruction methodology using both support vector machines (SVM) and relevanceExpand
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Support vector machines (SVMs) for monitoring network design.
In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized errorExpand
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Support vector machines for nonlinear state space reconstruction: Application to the Great Salt Lake time series
[1] The reconstruction of low-order nonlinear dynamics from the time series of a state variable has been an active area of research in the last decade. The 154 year long, biweekly time series of theExpand
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Basin scale water management and forecasting using artificial neural networks
: Water scarcity in the Sevier River Basin in south-central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficientlyExpand
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Support vectors–based groundwater head observation networks design
[1] This study presents a methodology for designing long-term groundwater head monitoring networks in order to reduce spatial redundancy. A spatially redundant well does not change the potentiometricExpand
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Effect of missing data on performance of learning algorithms for hydrologic predictions: Implications to an imputation technique
[1] A common practice in preprocessing of data for use in hydrological modeling is to ignore observations with any missing variable values at any given time step, even if it is only one of theExpand
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