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首页> 外文期刊>Electronics and Communications in Japan. Part 3, Fundamental Electronic Science >A Precipitation Estimation System Based on Support Vector Machine and Neural Network
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A Precipitation Estimation System Based on Support Vector Machine and Neural Network

机译：基于支持向量机和神经网络的降水量估算系统。

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摘要

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.
机译：为了提供防灾信息，例如有关泥石流，洪水和岩石雪崩的信息，准确预测局部突然降雨被认为很重要。但是，尚未解决该问题。本文考虑了这一问题，并提出了一种使用支持​​向量机和神经网络的降水估算系统。值得注意的是，降水是一个非常复杂的非线性事件，为此提出了一种多级估计系统来处理其复杂性。所提出的系统具有以下特征：支持向量机中的支持向量与原始特征空间中的评估数据之间的欧几里得距离用于估计降水。计算机实验表明，所提出的方法从实际测量值中得出评估数据的均方误差，这要比迄今为止报告的最佳结果更好。因此，证明了所提出的估计系统的有用性。

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