Paper:
Language: English:

A Data Mining Approach to Rainfall Intensity Classification Using TRMM/TMI Data

Shan-Tai Chen^, Shung-Lin Dou^, and Wann-Jin Chen^**

^*Dept. of Computer Science, Chung Cheng Institute of Technology, National Defense University
No.190, Sanyuan 1^st St., Tashi, Taoyuan, Taiwan, R.O.C.
^**Dept. of Environment Information Engineering, Chung Cheng Institute of Technology, National Defense University

Received: June 7, 2008

Accepted: August 20, 2008

Keywords: data mining, classification, rainfall intensity, TRMM, microwave

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.12, No.6 pp. 516-522, 2008

Abstract

The systematic approach we propose for classifying oceanic rainfall intensity during the typhoon season consists of two major steps – 1) identifying the rain areas and 2) classifying rainfall intensity into normal and heavy for these areas. The heterogeneous hierarchical classifier (HHC), an ensemble model we developed for accurately identifying heavy rainfall events, consists of a set of base classifiers. The base classifiers are independently constructed through heterogeneous data mining approaches such as artificial neural networks, decision trees, and self-organizing maps.
The meteorological satellite Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) data from 2000 to 2005 are used to create the classification models. TRMM precipitation radar (PR) data and rain gauge data from Automatic Rainfall and Meteorological Telemetry System (ARMTS) measurement are used as ground truth data to evaluate models. Two thirds of the dataset is used for model training and one third for testing. Experimental results show that the proposed model classifies rainfall intensity highly accurately and outperforms previously published methods.

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