Kuolin Hsu

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: 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.

TL;DR: In this paper, the authors present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets.

TL;DR: In this paper, a new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies, which addresses the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability.

TL;DR: In this article, a satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described.