基于神经网络的应急决策支持系统地形可通行性评价方法

A. Pershutkin, A. Dukhanov, Petr Gladilin
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引用次数: 0

摘要

本文提出了一种基于地形起伏、城市地物和水文气象参数的静态和动态资料估算运输单元速度的新方法。我们考虑了基于上述参数的现有和可用的越野路线方法(考虑根据地形参数降低速度),并定义了问题陈述。然后,我们设计了一种基于人工神经网络的地形表面速度评估方法。我们的方法考虑了地形的静态数据(如地表类型)和动态水文气象参数。为了确定该方法的效率,我们使用来自列宁格勒地区的数据进行了实验。实验结果表明,该方法大大提高了运输单元速度计算的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Approach to Terrain Trafficability Evaluation Based on a Neural Network for Emergency Decision-Support Systems
This paper deals with a new method to evaluate the velocity of a transport unit based on static and dynamic data on terrain relief, urban objects, and the values of hydrometeorological parameters. We considered the existing and available approaches to off-road routes based on the parameters mentioned above (considering velocity reduction depending on terrain parameters) and defined the problem statement. Then, we designed the method to evaluate the velocity on a terrain surface using an artificial neural network. Our method considers the static data (e.g., type of surface) and dynamic hydro-meteorological parameters of the terrain. To determine the efficiency of the method, we conducted the experiment using data from the Leningrad region. The experiment shows significantly increased accuracy in the velocity evaluation of the transport unit.
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