Knowledge-Based Geo-Risk Assessment for an Intelligent Measurement System - Artificial Intelligence in Theory and Practice III Access content directly
Conference Papers Year : 2010

Knowledge-Based Geo-Risk Assessment for an Intelligent Measurement System

Abstract

Rockfalls and landslides are major types of natural hazards worldwide that kill or injure a large number of individuals and cause very high costs every year. Risk assessment of such dangerous events requires an accurate evaluation of the geology, hydrogeology, morphology and interrelated factors such as environmental conditions and human activities. It is of particular importance for engineers and geologists to assess slope stability and dynamics in order to take appropriate, effective and timely measures against such events. This paper presents a decision-tool for geo-risk assessment on the basis of a knowledge-based system. The integration of such a tool with novel measurement sensors into an advanced system for geo-risk monitoring, which performs data fusion on-line, is innovative. To enable such a system, a knowledge base capturing domain knowledge formally is developed, which to the best of our knowledge is unprecedented; the completed part for initial risk assessment works quite well, as extensive experiments with a number of human experts have shown.
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hal-01058358 , version 1 (26-08-2014)

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T. Vicovac, A. Reiterer, U. Egly, T. Eiter, D. Rieke-Zapp. Knowledge-Based Geo-Risk Assessment for an Intelligent Measurement System. Third IFIP TC12 International Conference on Artificial Intelligence (AI) / Held as Part of World Computer Congress (WCC), Sep 2010, Brisbane, Australia. pp.215-224, ⟨10.1007/978-3-642-15286-3_21⟩. ⟨hal-01058358⟩
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