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The integration of artificial neural networks and geographic information systems for engineering geological mapping.

机译：人工神经网络和地理信息系统的集成，用于工程地质制图。

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摘要

This research investigates the utility of Artificial Neural Network (ANN) technology in producing Engineering Geological Maps. Specifically, a preliminary map describing the stability for sanitary landfill use was created. To accomplish this, three technologies were combined: ANNs, Geographic Information Systems (GISs), and engineering geological mapping techniques.;ANNs are computer programs designed to model the human brain and its ability to learn tasks. GIS are computer systems that store, analyze, and display mapped features and related information. Engineering geological mapping classifies the surficial materials and bedrock geology according to their suitability for engineered construction or modification. Engineering geology relies on basic geologic, hydrologic, and topographic data. This data is created, stored and displayed in the GIS, which is also used to format the data into the single-column vector needed by the ANN. Within the ANN, a sequence of training and testing was undertaken to create a tool that can be used to create preliminary maps of engineering geology themes. This is additional information for the engineering geologist to use in the investigation of a specific area. It is a tool that can reduce the level of effort, and thus the ultimate cost, of the engineering geological map.;This research shows that ANN technology is appropriate for creating preliminary engineering geological maps. The trained ANN correctly estimated the suitability for sanitary landfill use in 93% of the area being investigated.
机译：这项研究调查了人工神经网络（ANN）技术在生产工程地质图中的实用性。具体而言，创建了描述卫生垃圾填埋场使用稳定性的初步地图。为此，将三种技术结合起来：人工神经网络，地理信息系统（GIS）和工程地质制图技术。人工神经网络是旨在模拟人脑及其学习任务能力的计算机程序。 GIS是存储，分析和显示映射的要素和相关信息的计算机系统。工程地质制图根据表面材料和基岩地质的适合性，将其分类为工程构造或修改。工程地质依赖于基本的地质，水文和地形数据。这些数据是在GIS中创建，存储和显示的，还用于将数据格式化为ANN所需的单列向量。在人工神经网络内，进行了一系列培训和测试，以创建可用于创建工程地质主题初步图的工具。这是供工程地质学家用于特定区域调查的附加信息。它是一种可以减少工程地质图工作量，从而降低最终成本的工具。;这项研究表明，人工神经网络技术适合于创建初步的工程地质图。受过训练的人工神经网络正确地估计了93％的调查区域适合卫生垃圾填埋场使用。

著录项

  • 作者

    Easson, Gregory Lee.;

  • 作者单位

    University of Missouri - Rolla.;

  • 授予单位 University of Missouri - Rolla.;
  • 学科 Geotechnology.;Artificial Intelligence.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 1995
  • 页码 154 p.
  • 总页数 154
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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