An Artificial Intelligence Based Model For Optimal Exploratory Surveys
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An Artificial Intelligence based Model for Optimal Exploratory Surveys
Author: Aref Shirazi
language: en
Publisher: LAMBERT Academic Publishing
Release Date: 2024-01-01
exploratory surveys (geochemical and geophysical) are important and basic steps in the mineral exploration; The importance of this stage includes several reasons that are intertwined; First, the precision and accuracy of the measured data compared to the investigated community, whether they are representative of it or not, and can the data be used as representative of the community in future studies? Second, the density of exploratory surveys that is directly related to management resources (Time, cost, human and equipment); Third, the exact location of the exploration, which must be purposefully selected, so that optimal results can be obtained by spending the least amount of resources.
An Artificial Intelligence (AI)-based Model for Optimal Exploratory Surveys
Exploratory surveys (geochemical and geophysical) are important and basic steps in the mineral exploration; The importance of this stage includes several reasons that are intertwined; First, the precision and accuracy of the measured data compared to the investigated community, whether they are representative of it or not, and can the data be used as representative of the community in future studies? Second, the density of exploratory surveys that is directly related to management resources (Time, cost, human and equipment); Third, the exact location of the exploration, which must be purposefully selected, so that optimal results can be obtained by spending the least amount of resources.
Integration of Constraint Programming, Artificial Intelligence, and Operations Research
This book constitutes the proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization”.