The design of numerical prediction models system for the debris-flow disaster in Taiwan
Authors: Hsu-Yang Kung; Hao-Hsiang Ku; Ching-Yu Lin; Kuang-Jung Tasi
Journal:Chia Da Management Review. Jun. 2005, 25(2): 109-140.
Keywords: The design of numerical Prediction Models of Debris-Flow; Disaster Prevention; Back-propagation Network; Decision Support System; Mobile Multimedia Communications models system for the debris-flow disaster in Taiwanh
Abstract:
The effective disaster prediction is based on the correct debris-flow decision model and the real-time information communications
between the disaster area and the rescue-control center. In this paper, we proposed and designed a Real-time Mobile Debris
Flow Disaster Forecast system (RM(DF)^2), which is composed of the mobile clients, the application servers, and the decision
support server based on the wireless/mobile and Internet communications. Mobile clients use handheld devices, e.g., PDA
combining a cellular phone, to transmit and receive multimedia debris-flow information via the GSM/GPRS network. The
application server, which is composed of a Virtual-Reality manager and seven intelligent agents, provides the debris-flow VR
emulation and the customized information with mobile users. The customized operations could effectively reduce the bandwidth
consumption of the mobile network and release the computing load of handheld devices. We proposed three effective debris-flow
prediction models and the inference engine in the decision support server. The proposed prediction models are based on the
linear regression, the multivariate analysis, and the back-propagation network schemes. To have a practical simulation
environment, the database of the decision support server is the pre-analyzed 181 potential debris-flows in Taiwan. According
to the simulation results, the prediction model of adopting the back-propagation network scheme achieves the effective
debris-flow prediction with high degrees of accuracy. We also defined eight prediction factors of debris-flows, which can be
extracted using GPS, GIS, and Satellite Remote Sensing (RS) techniques, as the parameters of prediction models. The
implementation results of the RM(DF)^2 system reveal that the proposed prediction models and system architecture are feasible
and could achieve effective prediction and presentation of debris flows.