P
Pan Yang
Researcher at University of Illinois at Urbana–Champaign
Publications - 25
Citations - 341
Pan Yang is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Environmental science & Biology. The author has an hindex of 7, co-authored 19 publications receiving 189 citations. Previous affiliations of Pan Yang include Hong Kong University of Science and Technology & Beijing Normal University.
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Journal ArticleDOI
Crowdsourcing Methods for Data Collection in Geophysics : State of the Art, Issues, and Future Directions
Feifei Zheng,Ruoling Tao,Holger R. Maier,Holger R. Maier,Holger R. Maier,Linda See,Dragan Savic,Tuqiao Zhang,Qiuwen Chen,Thaine H. Assumpção,Pan Yang,Pan Yang,Bardia Heidari,Jörg Rieckermann,Barbara S. Minsker,Weiwei Bi,Ximing Cai,Dimitri Solomatine,Ioana Popescu +18 more
TL;DR: A review of the state of the art in this field can be found in this article, where the authors present a framework for categorizing the methods used in the seven domains of geophysics considered in this review.
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Assessment of Contributions of Climatic Variation and Human Activities to Streamflow Changes in the Lancang River, China
TL;DR: In this article, the authors used the Back-Propagation Artificial Neural Network (BP-ANN) model to reconstruct natural streamflow in the Lancang River and investigated the contributions of climatic variations and human activities at the yearly, seasonal and monthly time scales.
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Redefining marginal land for bioenergy crop production
Madhu Khanna,Luoye Chen,Bruno Basso,Ximing Cai,John L. Field,John L. Field,Kaiyu Guan,Chongya Jiang,Tyler J. Lark,Tom L. Richard,Tom L. Richard,Seth A. Spawn-Lee,Pan Yang,Katherine Y. Zipp +13 more
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Gauging Through the Crowd: A Crowd-Sourcing Approach to Urban Rainfall Measurement and Storm Water Modeling Implications
Pan Yang,Tze Ling Ng +1 more
TL;DR: In this paper, the authors explore the potential of crowd-sourcing for urban rainfall monitoring and the subsequent implications for storm water modeling through a series of simulation experiments involving synthetically generated crowd-source rainfall data and a storm water model.
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Machine learning based estimation of land productivity in the contiguous US using biophysical predictors
Pan Yang,Qiankun Zhao,Ximing Cai +2 more
TL;DR: In this article, the authors provided land productivity estimates in the contiguous United States (CONUS) through a machine learning approach, which is defined as the potential in producing agricultural outputs given biophysical properties including climate, soil, and land slope.