Decision aid expert system for Russian Arctic sustainable development under climate change

. Recently, socio-economic development of the Russian Arctic and Subarctic have form of planning and implementation of a set of large infrastructure projects under climate change. Currently, many businesses are planning and implementing a wide range of information technologies, including area of natural risks management, where it is required the development of new technologies and tools. The purpose of research was development of geo-information support system for managerial decision while natural risks management in Russian Arctic under climate change. Data bases and tools of different geo-information digital online platforms was used in research. There are demonstrated possibilities of developed geo-information support system with examples for area around the liquid natural gas product factory at Russian arctic seaport of Sabetta. As essential result, it is proposed to use remote sensing as basement for natural risks management within socio-economic development of the Russian Arctic. Presented research results can be useful for different users.


Introduction
An important area of Russia' socio-economic development is the state planning for the Arctic Zone Russian Federation (AZRF) development, which is seen as synergetic alliance of future large arctic projects (LAPs), as infrastructural and natural-industrial. Last regulatory documents use conception of sustainable development (SD) for Arctic Zone Russian Federation (AZRF), in contrast to earlier versions based on the concept of rational nature management (RNM). Now, socio-economic development of the Russian Arctic and Subarctic takes place in the form of planning and implementation of a set of large infrastructure energy projects (LIEPs). Recently, total cost for nearest such projects can be estimated in hundreds billion ns of US dollars (USD). An important feature of mentioned LIEPs' development is that it occurs under climate change (CC). Currently, many businesses are planning and implementing a wide range of information technologies [1][2][3][4], including area of natural risks management (NRM) [5][6][7][8][9], where it is required the development of new technologies and tools, for different objectives [10][11][12][13][14][15][16].
The purpose of research was development of geo-information support system (GISS) for managerial decision (MD) while LIEPs. In paper, there are described the development results of GISS for MD while LIEPs in Russian Arctic under CC.

Materials and Methods
Within research, there was used data bases and tools of geo-information digital online platform (GIDOP) Earth Observation Systems (EOS) eos.com, including its Land Viewer (LV) product. For visualization of modelled with supercomputers geo-data , there was used GIDOP Earth https://earth.nullschool.net .

Results
As a result of performed situational analysis, we propose that national LIEPs' activity in the Russian Arctic and Subarctic at the present stage should be implemented as an interconnected set of LIEPs within a single space-time area and with common geoinformation and management support As research result, it is proposed to use EOS within GISS, because it has efficient tools for processing satellite data from most of comprehensive space systems including Sentinel-2. In figures 1 and 2, we show the space image from SEOM Sentinel-2 for area around LNG product factory at Russian arctic seaport of Sabetta from Sentinel-2 on 22 June 2021, visualized with different applications of GIDOP EOS (LV product) and for different scales. Note, there is a LNG tanker under loading in red oval in figure 2.   Thus, we implemented remote sensing data into GISS for MD while LIEPs. Note, decoding of figures 1-4 and discussion of remote sensing data using into GISS for MD while LIEPs is not task of this article.

Discussion
Proposed above remote sensing implementation data into GISS for MD while LIEPs can be used in educational and training purposes. The essential task of university practical learning in NRM area is to teach students the practical aspects of work with above mentioned methods, which requires a developed learning base within special geo-information systems (GIS) laboratory.

Conclusions
In article, there are described development results of GISS for MD while LIEPs under CC. In paper, there was demonstrate possibilities of remote sensing implementation into GISS for MD while LIEPs with examples for area around the LNG product factory at Russian arctic seaport of Sabetta. As essential result, we propose to use remote sensing implementation into GISS for MD while LIEPs, as basement for NRM in LIEPs, including educational and training purposes. The presented research results have significant scientific novelty and can be useful for different users.