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Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling
We present a diagnostically interesting decomposition of NSE (and hence MSE), which facilitates analysis of the relative importance of its different components in the context of hydrological modelling and show how model calibration problems can arise due to interactions among these components. Expand
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Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration
The usefulness of a hydrologic model depends on how well the model is calibrated. Expand
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Evaluation of PERSIANN system satellite-based estimates of tropical rainfall
Abstract PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall fromExpand
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Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information
This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. Expand
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A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters
Author(s): Vrugt, JA; Gupta, HV; Bouten, W; Sorooshian, S | Abstract: Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution ofExpand
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Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks
Abstract A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of thisExpand
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Dual state-parameter estimation of hydrological models using ensemble Kalman filter
A dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. Expand
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Artificial Neural Network Modeling of the Rainfall‐Runoff Process
An artificial neural network (ANN) is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. ANN models have been foundExpand
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Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods
Automatic methods for model calibration seek to take advantage of the speed and power of digital computers, while being objective and relatively easy to implement. Expand
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Multi-objective global optimization for hydrologic models
The MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objective global optimization problem, is presented in this paper. Expand
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