Rainstorm forecasting by mining heterogeneous remote sensed datasets
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AbstractThis chapter presents an automatic meteorological data mining system based on analyzing and mining heterogeneous remote sensed image datasets, with which it is possible to forecast potential rainstorms in advance. A two-phase data mining method employing machine learning techniques, including the C4.5 decision tree algorithm and dependency network analysis, is proposed, by which a group of derivation rules and a conceptual model for metrological environment factors are generated to assist the automatic weather forecasting task. Experimental results have shown that the system reduces the heavy workload of manual weather forecasting and provides meaningful interpretations to the forecasted results. © 2010, IGI Global.
All Author(s) ListYang Y.-B., Lin H.
All Editor(s) Listed. by Leon Shyue-Liang Wang & Tzung-pei Hong.
Detailed descriptioned. by Leon Shyue-Liang Wang & Tzung-pei Hong.
Year2010
Month12
Day1
Pages387 - 404
ISBN9781615207572
LanguagesEnglish-United Kingdom

Last updated on 2020-01-07 at 02:21