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JDR Vol.9 No.1 pp. 42-47
(2014)
doi: 10.20965/jdr.2014.p0042

Paper:

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Typhoon Economic Loss Prediction in China by Applying General Regression Neural Network and Hierarchical Cluster Analysis

Bo Cheng*, Ling Cheng**, and Lingmin Jiang*

*School of Informatics, Guangdong University of Foreign Studies, Guangzhou 510420, China

**School of Electrical and Information Engineering, University of the Witwatersrand, Private Bag 3, Wits. 2050, Johannesburg, South Africa

Received:
September 24, 2013
Accepted:
December 26, 2013
Published:
February 1, 2014
Creative Commons License
Keywords:
general regression neural network, hierarchical cluster analysis, typhoon, loss prediction
Abstract
Natural disasters may cause extreme damage and enormous economic loss. It is important to look for efficient and precise damage prediction models using neural networks, which are increasingly used in many applications. One challenge of developing such a damage prediction model is its limited amount of available data. We therefore chose to predict typhoon damage loss based on a general regression neural network (GRNN). The GRNN is able to converge to kernel functions of data with limited training samples available. This paper investigates a GRNN-based neural network and introduces a loss prediction index. The proposed GRNN structure gives an improved prediction performance with a normalized mean squared error of 0.0071 and a correlation of 0.9321. According to prediction results of economic loss, 30 typhoons have been grouped into five categories by hierarchical cluster analysis. Due to its simplicity and fast-converging features, this scheme is suitable for practical, simple but robust typhoon damage prediction.
Cite this article as:
B. Cheng, L. Cheng, and L. Jiang, “Typhoon Economic Loss Prediction in China by Applying General Regression Neural Network and Hierarchical Cluster Analysis,” J. Disaster Res., Vol.9 No.1, pp. 42-47, 2014.
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Last updated on Apr. 19, 2024