Rainstorm Forecasting By Mining Heterogeneous Remote Sensed Datasets

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Author(s): Yu-Bin Yang (State Key Laboratory for Novel Software Technology, China)and Hui Lin (The Chinese University of Hong Kong, Hong Kong)
Copyright: 2010
Pages: 18
Source title: Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technologies
Source Author(s)/Editor(s): Leon Shyue-Liang Wang (National University of Kaohsiung, Taiwan)and Tzung-Pei Hong (National University of Kaohsiung, Taiwan)
DOI: 10.4018/978-1-61520-757-2.ch018

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

This 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.

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Rainstorm Forecasting By Mining Heterogeneous Remote Sensed Datasets

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