Informatics and Applications
2019, Volume 13, Issue 3, pp 131-136
DEVELOPMENT OF A METHOD FOR THE FORMATION OF ATTRIBUTE SPACE AND A MODEL FOR THE ASSESSMENT AND PREDICTION OF ANTHROPOGENIC INFLUENCE ON THE ENVIRONMENT (ON THE EXAMPLE OF THE FOREST FUND OF THE OIL-PRODUCING REGION)
- V. V. Burlutskiy
- A. V. Yakimchuk
- A. V. Melnikov
- A. L. Tsaregorodtsev
- S. V. Voloshin
Abstract
The work is devoted to the development of a systematic method for assessing and predicting the influence of natural and anthropogenic impacts on the environment, including the procedures for the transformation of initial data store, the formation of the neural network model, its training, and testing. The method is used to analyze the consequences of anthropogenic impacts on the environment in the Khanty-Mansiysk Autonomous Okrug -
Yugra.
[+] References (10)
- Guikema, S. D. 2009. Natural disaster risk analysis for critical infrastructure systems: An approach based on sta-tistical learning theory. Reliab. Eng. Syst. Safe. 94(4):855- 860. doi: 10.1016/j.ress.2008.09.003.
- Shokin, Y I., V.V. Moskvichev, and V.V. Nicheporchuk. 2010.Metodika otsenki antropogennykh riskov territoriy i postroeniya kartogramm riskov s ispol'zovaniem geoin- formatsionnykh system [Technique for estimation of an- thropogenous risks for territories and construction of risks cartograms using geoinformation systems]. Computational Technologies 15(1):120-131.
- Gumenyuk, V. I., A. M. Karmishin, and V. A. Kireev. 2013.
O kolichestvennykh pokazatelyakh opasnosti tekhnogen- nykh avariy [About quantitative indicators of danger man-made accidents]. Nauchno-tekhnicheskie vedomosti SPbPU [St. Petersburg State Polytechnic University J. Engineering Science Technology] 2(171):281-288.
- Kolesenkov, A. N., B. V. Kostrov, and V. N. Ruchkin. 2014. Neyronnye seti monitoringa chrezvychaynykh situatsiy po
dannym DZZ [Neural network monitoring for emergencies according ERS]. Izvestiya Tul'skogo gosudarstvennogo universiteta. Tekhnicheskie nauki [Transactions of Tula State University. Technical Sciences] 5:220-225.
- Stone, C.J. 1977. Consistent nonparametric regression. Ann. Stat. 5(4):595-620.
- Breiman, L. 2001. Random forests. Mach. Learn. 45(1):5- 32.
- Robbins, H., and D. Siegmund. 1971. A convergence the-orem for non negative almost supermartingales and some applications. Optimizing methods statistics. Ed. J. S. Rusta- gi. New York, NY: Academic Press. 233-257.
- Cortes, C., and V. Vapnik. 1995. Support-vector networks. Mach. Learn. 20(3):273-297.
- Hong, H., B. Pradhan, andM. N. Jebur. 2016. Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environ. Earth Sci. 75(1):40.
- Kohavi, R. 1995. A study of cross-validation and boot-strap for accuracy estimation and model selection. 14th Joint Conference (International) on Artificial Intelligence Proceedings. Montreal, Quebec, Canada. 2:1137-1143.
[+] About this article
Title
DEVELOPMENT OF A METHOD FOR THE FORMATION OF ATTRIBUTE SPACE AND A MODEL FOR THE ASSESSMENT AND PREDICTION OF ANTHROPOGENIC INFLUENCE ON THE ENVIRONMENT (ON THE EXAMPLE OF THE FOREST FUND OF THE OIL-PRODUCING REGION)
Journal
Informatics and Applications
2019, Volume 13, Issue 3, pp 131-136
Cover Date
2019-09-30
DOI
10.14357/19922264190318
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
data analysis; machine learning; neural networks; spatial analysis; geographic information systems; risk-based approach; control and supervision
Authors
V. V. Burlutskiy , A. V. Yakimchuk , A. V. Melnikov , A. L. Tsaregorodtsev , and S. V. Voloshin
Author Affiliations
Yugra Research Institute of Information Technologies, 151 Mira Str., Khanty-Mansiysk 628011, Russian Federation
Yugra State University, 16 Chekhova Str., Khanty-Mansiysk 628012, Russian Federation
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