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A New Disaster Monitor and Forecast System Based on RBF Neural Networks

机译：基于RBF神经网络的新型灾害监测预报系统

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摘要

A new disaster monitor and forecast system based on RBF neural networks is proposed. This disaster forecast system consists of disaster spatial monitor subsystem that is pre-trained by off-line learning algorithms and disaster time forecast subsystem developed by online learning algorithms. The disaster spatial monitor subsystem aims to detect trend of the objective behavior, once the unstable condition is detected, the real time series will be collected and used to forecast disaster by the disaster time forecast subsystem. Using real time data on the eve of disaster, which contain more information about the disaster, this system can largely improve the pertinence and guarantee the accuracy of disaster forecast. To illustrate the feasibility and effectiveness, this system is applied to the landslide forecast. Simulation results show that the spatial monitor subsystem can effectively forecast the stability of the various monitoring points and the time forecast subsystem established by online learning algorithms has better forecast precision as compared with the subsystem established by the Orthogonal Least Squares algorithm. The results also illustrate that the system holds high precise if real time date are sufficient and has a broad application prospects.
机译：提出了一种基于RBF神经网络的灾害监测预警系统。该灾难预测系统包括由离线学习算法预先训练的灾难空间监视子系统和由在线学习算法开发的灾难时间预测子系统。灾难空间监控子系统旨在检测目标行为的趋势，一旦发现不稳定情况，实时时间序列将被收集并由灾难时间预测子系统用于预测灾难。该系统使用灾难前夕的实时数据（其中包含有关灾难的更多信息），可以在很大程度上提高针对性并保证灾难预测的准确性。为了说明可行性和有效性，将该系统应用于滑坡预测。仿真结果表明，空间监测子系统可以有效地预测各个监测点的稳定性，在线学习算法建立的时间预测子系统与正交最小二乘算法建立的子系统相比具有更好的预测精度。结果还表明，如果实时数据足够，该系统将具有很高的精度，并具有广阔的应用前景。

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