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16- ЕИЭМПЗ / ТПИ. - Томск, 1979, часть 2. - 150 с. - Деп. в ВИНИТИ 5.07.79, №4297-79.
17. Холодов Ю.А. Реакция нервной системы на электромагнитные поля. - М.: Наука, 1975. - 203 с.
18. Гутнов А.Э. Будущее науки о городе // Природа. -1984. - № 12. - С. 74-83.
19. Свиришев Ю.М., Гидаури А.А., Разжевайкин В.Н. Волны в экологии // Нелинейные волны (самоорганизация).
- М.: Наука, 1983. - С. 32-46.
20. Иличкин В. Токсикологическая экспертиза // Пожарное дело. - 1985. - № 3. - С. 27-28.
21. Боев С.Г. Заряжение диэлектриков электромагнитным облучением и формирование радиоэлектретного эффекта: Автореф. дисс. ... канд. физ.-мат. наук. - Л., 1978. - 15 с.
22. Сальников В.Н. Изменение электропроводности и регистрация электромагнитных импульсов в предваритель-
но облученных электронами образцах горных пород при их нагревании. // Изв. АН СССР. Физика Земли. - 1979. - № 2. - С.79-92.
23. Актуальные вопросы биоповреждений / Под ред. Б.В. Бочарова. - М.: Наука, 1983. - 138 с.
24. Сальников В.Н. Электромагнитные системы литосферы и техногенеза (аномальные явления) / Томск, 1991. 384 с. Деп. В ВИНИТИ 18.03.91, №1156 - В91.
25. Мананков А.В. Геологическая среда и техносфера: квантовые процессы и жизнь. Самоорганизация. - Томск: Изд-во ТГАСУ, 2012. - 416 с.
26.Сальников В.Н. Потылицына Е.С.Геология и самоорганизация жизни на Земле. - Томск: Изд-во ТПУ,2013.-430с.
RISK ANALYSIS, EVALUATION AND MANAGEMENT FOR LANDSLIDE
PROCESSES
Svalova V.B.
Sergeev InMitute of Environmental Geoscience RAS, Moscow, Leading scientiM
ABSTRACT
Methodology for risk evaluation and risk map contraction for urbanized territories on the example of Moscow is elaborated and suggefled.
Keywords: risk, landslide, map, damage, hazard, danger, vulnerability
Geological risk maps are the way and important flep towards solving the problem of natural risk analysis and management. (Corominas et al. 2014, Natural hazards of Russia. Evaluation and management of natural risks, 2003)
Due to the complexity and diversity of the problem the queflion of a combination of probabiliflic and determiniflic approaches and the use of expert eflimates arises.
Thus, the probability of the landslide process depends on the flability of the landslide slope, trigger mechanisms (precipitation, earthquakes), technological factors. Ideally, you firfl need to fludy the physical and mechanical sliding process in different conditions. But mechanics of landslide process is flill not fully underflood. Prediction of landslide event is not always possible. Even Satirical frequency of activation of landslides for a particular area varies very widely.
As an example let us consider the approach to the contraction of the map of landslide risk in the territory of Moscow. Landslide processes in Moscow are well invefligated (Moscow. Geology and town.1997; Pofloev & Svalova 2005; Svalova 2011-2015; Svalova & Pofloev 2008). Landslides cover about 3% of the city, where there are 15 deep and a lot of small landslides. The maps of landslide hazard areas are contracted (Moscow. Geology and town.1997). Recently in Moscow there is a significant activation of landslide processes. Completed landslide measures significantly diflort the natural pattern. But to assess the potential landslide hazard the height of the slope, the landslide body volume, mass velocity, rock properties, topography of the surrounding area, the range of possible promotion landslide masses, hydrogeological conditions, trigger mechanisms can be taken into account. Experienced landslide researchers are capable of giving highly accurate comparative assessment of landslide hazard for different slopes at Moscow area. Selection
of taxons (special areas) varying degrees of landslide hazard in the city is completely solvable task. And gradation is possible as in the three degrees of danger (high, medium, low) as in five ones (very high, high, medium, low, not dangerous), depending on the detail of the task.
The mofl expensive land and buildings in Moscow are located in the city center. There are also the oldefl hifloric buildings, the mofl vulnerable to natural hazards. There are also the mofl expensive new ground and underground contraction, subway lines, complex traffic and technical communications of high density. There is an increased density of population and the people in the daytime. We can assume that the closer to the center of Moscow, the greater the potential damage from possible landslide process.
Hazardous induflrial production brought to Moscow's periphery. But the protected zone of Moscow on the Vorobiovy Hills and in Kolomenskoye also have high inventory and cultural value, and the potential damage there is highly evaluated. So to a firfl approximation map of landslide risk in Moscow may be an overlay of landslide hazard maps and population density, building density, land prices, density of roads and infraflructure maps. Areas with the highefl degree of landslide hazard and the highefl damage are the areas of the highefl landslide risk in the territory of Moscow.
The next methodology for risk evaluation and mapping is suggefled.
Methodology of contraction of landslide risk maps
For the automated analysis of the factual material and the risk maps contraction it is needed to find the intersection of the landslide hazard map and integrated map of possible damage t.e. for each i - th fragment Ri of risk map to find the product of
probability Pi of landslide event to the amount of different j - th possible damages from landslides:
Ri = Pi • Dij
Maps of landslide hazard it is necessary to calibrate from 0 to 1, so far as possible to reflect the probability of landslide events. Thus, gradation, for example, is possible on a scale of (0; 0,25; 0,5; 0,75; 1), where 0 corresponds to no danger of landslides, 0.25 - low, 0.5 - average 0.75 - high and 1 - a very high probability of the landslide process. This assessment is an expert in nature. In principle it is possible to formalize and contraction of landslide hazard maps and to consider it as the intersection of maps of factual material, such as map of relief contrafl, rock flrength, slope lability, speed of motion of the surface, the density of rainfall, seismicity, etc. Of course, this will require additional research and evaluation.
For a comprehensive assessment of the damage in each region it is suggefled to calibrate the possible damage of each option on a three-point syflem (0, 1, 2), where 0 means no damage, 1 - middle, 2 - high damage. The parameters here are, for example, 1) the cofl of land, 2) the cofl of housing, 3)
density f buildings, 4) population density, 5) the density of roads and communications. The higher the value (the value of land, housing, etc.), the greater the damage in case of a hazardous event.
Then, the possible damage to 5 parameters for each element varies from 0 to 10.
The risk also in each element ranges from 0 to 10. This is the risk in relative terms (high-low), on 10-point scale.
After defeating the map of the area into squares and calculating the risk for each square, you can get a map of the area at risk on 10-point scale.
On the basis of preliminary expert eflimates, it will be the areas in the vicinity of Moscow River and Yauza River, as well as in the areas of contrafling relief along riverbeds of paleorivers in the city center.
The places of high landslide risk are Andronievskaya embankment (Figs. 1, 2), Nikolo-Yamskaya embankment (Fig. 3), Kotelnicheskaya embankment (Figs. 4, 5), Samotechnaya Street (Fig. 6 ) in the center of Moscow.
Fig. 1. Andronievskaya embankment with Svjato-Andronikov monaflery.
Fig. 3. Nikolo-Yamskaya embankment
Fig. 5. Kotelnicheskaya embankment with high building.
Fig. 6. Samotechnaya Street.
The places of highefl landslide risk are Vorobiovy Mountains (Hills) (Figs. 7-10) and Kremlin Hill. (Figs. 11-13). They are
shown as white circles in the map of geological danger in Moscow. (Fig. 14).
Fig. 7. Vorobiovy Mountains with Moscow State University, ski-jumps and metro-bridge.
Fig. 9. Vorobiovy Mountains with Andreevsky monaflery and new living houses.
Fig. 11. Moscow Kremlin.
Fig. 13. Center of Moscow with Kremlin hill and Moscow river.
These areas may be considered as «hot spots» on the risk map. And if in some of these points, the population density is not so high, the other components (cofl of land, the hiflorical
importance of the object, the density of underground utilities and others) give a great contribution to the high risk assessment.
Fig. 14. Map of geological danger in Moscow. Landslides, karfl, underflooding. (Osipov VI., Kutepon V.M. , Mironov O.K. (Moscow. Geology and town.1997 ) Landslides are near rivers in red and pink. 1 - very high danger, 2 - high, 3 - middle, 4 - low, 5 - no. White circles - risk "hot spots". Kremlin hill (center) and Vorobiovy Mountaims (south-eafl).
As additional fact it is interefling to use night cosmic and aero photos of Moscow that reflect the density of communications and possible damage. (Figs. 15-17).
Fig. 16. Cosmic photo of Moscow in night.
Fig. 17. Cosmic photo of Europe with Moscow in night.
The problem of geological risk management is seen as series of events leading to risk reduction, including the organization of an integrated environmental monitoring syflem.
References
1. Corominas J, van Weflen C, Frattini P, Cascini L, Mallet J-P et al. (2014) Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and Environment. V 73, №2, 209-263.
2. Moscow. Geology and town.(1997). Ed. by Osipov and Medvedev. M., Moscow textbooks and kartolitografiya, 400 p.
3. Natural hazards of Russia. Evaluation and management of natural risks.(2003). Ed. by Ragozin A.L. M., KRUK, 316 p.
4. Pofloev GP, Svalova VB (2005) Landslides risk reduction and monitoring for urban territories in Russia. Proceedings of the Firfl General Assembly of ICL (International Consortium on Landslides), "Landslides: risk analysis and suflainable disafler management", Washington, USA, Springer, pp 297-303.
5. Svalova VB (2011) Mechanical-mathematical modeling and monitoring for landslide processes. Journal of Environmental Science and Engineering. V 5, N 10, 1282-1287.
6. Svalova V (2011) Monitoring and modeling of landslide processes. Monitoring. Science and technology. №2(7), 19-27. (in Russian).
7. Svalova VB (2011) Landslide process simulation and monitoring. Proceedings of ENGEOPRO, Moscow, 7pp.
8. Svalova VB (2012) Mechanical-mathematical modeling and monitoring for landslides. Proceedings of IPL (International Program on Landslides) Symposium, UNESCO, Paris, 63-68.
9. Svalova VB (2013) Risk reduction for landslide hazards. Modeling and monitoring, Proceedings of The International Conference Natural risks: analysis, eflimation, mapping, Moscow, MSU, 157-163. (In Russian).
10. Svalova VB (2014) Modeling and Monitoring for Landslide Processes. Chapter in book: Natural Disaflers -Typhoons and Landslides - Risk Prediction, Crisis Management and Environmental Impacts. Editor: K. Linwood, Nova Science Publishers, NY USA, p. 177-198.
11. Svalova VB (2014) Mechanical-mathematical modeling and monitoring for landslide processes. IPL 163 Project. Proceedings of the World Landslide Forum 3. Volume 4. Beijing, China, p.24-27.
12. Svalova VB (2014) Modeling and monitoring for landslide processes: case fludy of Moscow and Taiwan. Proceedings of the World Landslide Forum 3. Volume 4. Beijing, China, p.628-632.
13. Svalova VB (2015) Mechanical modeling and geophysical monitoring for landslide processes. Proceedings of IAEG XII Congress "Engineering geology for society and territory" , v.2, Torino-2014, Italy, Springer, 345-348.
14. Svalova V, Pofloev G (2008) Landslide Process Activization on Sites of Cultural Heritage in Moscow, Russia. Proceedings of the Firfl World Landslide Forum 2008, Tokyo, Japan, 4p.
