Developing Risk Assessment Model for Altering Conditions of Forest Reserves in an Oil-Production Region

DOI

10.2991/aisr.k.201029.059How to use a DOI?

Keywords

data analysis, machine learning, neural networks, spatial analysis, geographic information systems, risk-based approach

Abstract

The scientific problem, the solution of which is aimed at this work, is the implementation of a systematic method of assessing and predicting the influence of anthropogenic impact on the natural environment of the oil-producing region. The article considers the process of manipulating specific heterogeneous data and presents an implemented neural network model for predicting areas with the most likely risk of oil spill. A special feature of the proposed approach is the use of hybrid methods of machine learning in conjugation with the geo information analysis on the basis of the history of incidents occurred on sections of license areas. Some statistical estimates of the influence of the assessed factors of risk formation on an emergence probability of an incident have been obtained. Within the framework of this investigation, a vector-based description of signs of incidents was formulated and validated followed by a forecast based on methods of machine learning.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Alexander Yakimchuk
AU  - Andrey Melnikov
AU  - Vladimir Burlutskiy
AU  - Alexander Tsaregorodtsev
PY  - 2020
DA  - 2020/11/10
TI  - Developing Risk Assessment Model for Altering Conditions of Forest Reserves in an Oil-Production Region
BT  - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020)
PB  - Atlantis Press
SP  - 313
EP  - 317
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.201029.059
DO  - 10.2991/aisr.k.201029.059
ID  - Yakimchuk2020
ER  -