Remote sensing precipitation using artificial neural networks and machine learning methods
2019 Spring Cyberseminar Series
- Kuolin Hsu / University of California, Irvine
Satellite remote sensing techniques provide a unique way to monitor precipitation at a global scale, especially for regions where ground measurements are limited. Recent development in computational Intelligence has shown excellent progress in learning from a large amount of in situ and remote sensing data to improve the quality of precipitation measurement. In this presentation, integrate multi-satellite sensors for precipitation estimation using deep neural networks (DNNs) and recent machine learning methods developed at the Center for Hydrometeorology (CHRS) will be presented. Case studies will be demonstrated for monitoring of precipitation from extreme storm events.
2019 Spring Cyberseminar Series: Recent advances in big data machine learning in Hydrology
Hosted by Chaopeng Shen, Pennsylvania State University
Recently big data machine learning has led to substantial changes across many areas of study. In Hydrology, the introduction of big data and machine learning methods have substantially improved our ability to address existing challenges and encouraged novel perspectives and new applications. These advances present new opportunities methods that aid scientific discovery, data discovery, and predictive modeling. This series cover new techniques and findings that have emerged in Hydrology during the previous year, with a focus on catchment and land surface hydrology.