Knowledge Discovery Lab


The development of disastrous flood forecasting techniques able to provide warnings at a long lead-time (5-15 days) is of great importance to society. Extreme Flood is usually a consequence of a sequence of precipitation events occurring over from several days to several weeks. The long-term forecasting of precipitation clusters can be attempted by identifying persistent atmospheric regimes that are conducive for the precipitation clusters. However, such forecasting will suffer from overwhelming number of relevant features and high imbalance of sample sets. In this study, we propose an integrated data mining framework for identifying the precursors to precipitation event clusters and use this information to predict extended periods of extreme precipitation and subsequent floods. We synthesize a representative feature set that describes the atmosphere motion, and apply a streaming feature selection algorithm to online identify the precipitation precursors from the enormous feature space. A hierarchical re-sampling approach is embedded in the framework to deal with the imbalance problem.


2013, Towards long-lead forecasting of extreme flood events: a data mining framework for precipitation cluster precursors identification

Dawei Wang, Wei Ding, Kui Yu, Xindong Wu, Ping Chen, David L. Small, Shafiqul Islam

The 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Chicago, USA, August, 2013


Individuals Involved

Dawei Wang, Wei Ding, Kui Yu, Xindong Wu, Ping Chen, David L. Small, Shafiqul Islam

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