A precipitation estimation system based on support vector machine and neural network

Abstract

For the purpose of providing disaster prevention information, such as information concerning mudslides, flooding, and rock avalanches, it is considered important to predict local sudden precipitation accurately. However, no solution to this problem has been achieved. This paper considers the problem and proposes a precipitation estimation system using a support vector machine and a neural network. It is noted that precipitation is a very complex nonlinear event, and a multistage estimation system is proposed to deal with its complexity. The proposed system has the feature that the Euclidean distance between the support vector in the support vector machine and the evaluation data in the original feature space is used to estimate the precipitation. Computer experiments indicate that the proposed method gives a mean-square error of the evaluated data from the actually measured value which is better than the best result reported to date. Thus, the usefulness of the proposed estimation system is demonstrated. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 89(3): 38–47, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20226