Developing Risk Assessment Model for Altering Conditions of Forest Reserves in an Oil-Production Region
- https://doi.org/10.2991/aisr.k.201029.059How to use a DOI?
- data analysis, machine learning, neural networks, spatial analysis, geographic information systems, risk-based approach
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/aisr.k.201029.059 ID - Yakimchuk2020 ER -