NDLI: Developing machine learning tools for long-lead heavy precipitation prediction with multi-sensor data

Content Provider	IEEE Xplore Digital Library
Author	Yahui Di Wei Ding Yang Mu Small, D.L. Islam, S. Ni-Bin Chang
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Massachusetts Boston, Boston, MA, USA (Yahui Di; Wei Ding; Yang Mu) \|\| Dept. of Civil & Environ. Eng., Tufts Univ., Medford, MA, USA (Small, D.L.; Islam, S.) \|\| Dept. of Civil, Univ. of Central Florida, Orlando, FL, USA (Ni-Bin Chang)
Abstract	A large number of extreme floods were closely related to heavy precipitation which lasted for several days or weeks. Long-lead prediction of extreme precipitation, i.e., prediction of 6-15 days ahead of time, is important for understanding the prognostic forecasting potential of many natural disasters, such as floods. Yet, long-lead flood forecasting is a challenging task due to the cascaded uncertainty with prediction errors from measurements to modeling, which makes the current physics-based numerical simulation models extremely complex and inaccurate. In this paper, we formulate the modeling work as a machine learning problem and introduce a complementary data mining framework for heavy precipitation prediction. Heavy precipitation that may lead to extreme floods is a rare event. Long-lead prediction requires the corresponding feature space to be sampled from extremely high spatio-temporal dimensions. Such a complexity makes long-lead heavy precipitation prediction a high dimensional and imbalanced machine learning problem. In this work, we firstly define the extreme precipitation and non-extreme precipitation clusters and then design the Nearest-Sample Choosing method to handle the imbalanced data sets. We introduce streaming feature selection and subspace learning to extract the most relevant features from high dimensional data. We evaluate the machine learning tools using historical flood data collected in the State of Iowa, the United States and associated hydrometeorological variables from 1948 to 2010.
Starting Page	63
Ending Page	68
File Size	687159
Page Count	6
File Format	PDF
ISBN	9781479980697
DOI	10.1109/ICNSC.2015.7116011
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-09
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Floods Forecasting Training data Predictive models Data mining Classification algorithms Accuracy Machine Learning Heavy Precipitation Predicition Nearest Sample Choosing Fast Online Streaming Feature Selection
Content Type	Text
Resource Type	Article

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