Hoshin V. Gupta

TL;DR: A diagnostically interesting decomposition of NSE is presented, which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and it is shown how model calibration problems can arise due to interactions among these components.

TL;DR: The capability of the shuffled complex evolution automatic procedure is compared with the interactive multilevel calibration multistage semiautomated method developed for calibration of the Sacramento soil moisture accounting streamflow forecasting model of the U.S. National Weather Service and suggests that the state of the art in automatic calibration now can be expounded.

TL;DR: In this paper, the authors presented a new procedure (entitled linear least squares simplex, or LLSSIM) for identifying the structure and parameters of three-layer feed forward ANN models and demonstrated the potential of such models for simulating the nonlinear hydrologic behavior of watersheds.

TL;DR: 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.

TL;DR: PERSIANN as discussed by the authors is an automated system for precipitation estimation from Remotely Sensed Information using Artificial Neural Networks, which is developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution of 0.25° × 0.75° every half-hour.