About Creating a Digital Twins in Field of Earth Sciences
EVGENII VIAZILOV 1
1 National Oceanographic Data Centre
All-Russian Research Institute for Hydrometeorological Information–World Data Centre
6, Koroleva, 249035. Obninsk
RUSSIAN FEDERATION
Abstract: - A brief analysis of the research results on the creation of digital twins in the field of earth sciences
presented. For the first time approaches proposed to create a digital twin for this field. The approximate
composition of the digital twin data for the development of hydrometeorological support for consumers has
been determined. Requirements for the digital twin developed. Digital twins should become key components
at the heart of smart buildings, cities, digital enterprises, self-driving cars, flying objects, ships, and others that
require data on the state of the environment and the state of these objects. The digital twin can be used to
impact models of the environment on enterprises, to solve tasks of calculating the possible damage and cost of
preventive actions in the event of the passage of disasters.
Key-Words: - Digital twin, earth sciences, development, using for hydrometeorological support
Received: March 27, 2022. Revised: October 24, 2022. Accepted: November 19, 2022. Published: December 31, 2022.
1 Introduction
The earth sciences data are big, complex and
diverse in structure, heterogeneous and distributed,
presented at different temporal and spatial
resolutions. This data goes through multi-stage
processing. At the same time, at any stage of
processing, the data can be used in various models
for: calculating new parameters, obtaining climate
generalizations; for modeling of processes in
various spheres of the earth, weather forecasts,
pollution spread, etc. To study climate change, the
impact of disasters on the population and
enterprises, an interdisciplinary approach based on
data integration is needed. Such opportunities open
up due to the expansion of the accessibility of data
on the environment, technical and economic
indicators of the work of enterprises, the socio-
economic state of the objects, etc.
In today's world, data must be accessible for
analysis no matter where it is located. Heads need
to get answers quickly to questions related, for
example, to the organization of preventive actions
before the disaster. For this, data integration is now
actively used. Integration tools together all the
necessary information to provide the consumers
with metadata-based data visibility and access to all
available data. Data integration eliminates the
information disunity of institutions organizing
hydrometeorological support (HMS). This allows
the delivery of complex information to any
consumer, accelerates the receipt of analytical
results necessary for making decisions.
Unfortunately, data integration, as a rule, is carried
out based on databases (DB) and data arrays that
present organizations, without taking into account
all the tasks of their use. The prospect of HMS
development is the widespread use of integrated
data in various models and the creation of digital
twins (DTs) for earth sciences with the inclusion of
additional information from other sciences.
There are several definitions of the term DT.
The most general definition is given in the ISO
23247 standard. “DT is a digital model of a specific
physical object or process with connected datasets
that provides convergence between physical and
virtual states at an appropriate synchronization
rate”. For each subject area, this definition is
refined, for example, in industry, a digital model is
understood as a virtual model that either describes a
real-life object or serves as a prototype of a future
object at the micro- and macro levels.
DT should be used throughout the life cycle of a
physical object to model various activities [1].
Analysis of DT behavior is able to identify problem
areas in a real object, which allows performing
proactive actions that prevent accidents and damage
[2]. DT will help to test different solutions and
choose the best option before head start
implementing them. DT allows support of business
processes depending on changing environmental
conditions, to work with the same information.
There are already many examples of DT
development and use [3-5]. So yet 20 years ago
there were projects called "Digital Earth Planet",
the result of which is, for example, the application
https://www.google.ru/intl/ru/earth/. Many
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.6
Evgenii Viazilov
E-ISSN: 2945-0454
42
Volume 1, 2022
countries have created Digital Terrain Model
(DTM), which is widely used in geographical
information systems. The DTM presents data for
the entire globe at spatial scales from 1:5,000,000 to
1:500. The military-industrial company Lockheed
Martin uses DT of real landscapes in the form of
DTM to fight forest fires. NVidia Company is
going to create a complete virtual copy of our
planet with all its natural and man-made processes,
and then begin to simulate potential climate change
[6]. The system will allow predicting climate
change on the planet for several decades to come,
creating models of the movement of water in the
oceans, sea ice, the earth's surface and groundwater.
The Arctic Labs digital platform was created in
Russia. Its basis is the DT of the Northern Sea
Route - a dynamic mathematical model, which
allows to explore scenarios for the development of
both the entire Arctic and individual territories,
logistics routes (for example, to determine the
logistics of delivery of oversized cargo from St.-
Petersburg to Magadan). Here, a number of
conditions should be taken into account, including
accounting for the navigational and
hydrometeorological situations throughout the
route. The system will work in real time and take
into account more than 10,000 parameters,
including Arctic resources, infrastructure, transport,
shipbuilding, and logistics, social, economic, and
environmental data. This will allow for a
comprehensive assessment of the feasibility of
investment and infrastructure projects, their impact
on the dynamics of the development of regions and
individual territories, and the Arctic economy on
Russia's gross domestic product.
The applications of DT in the field of
geosciences and data use are as follows:
Modeling and forecasting the state of the
environment;
Global climate change modeling based on data
for air temperature, sea level, precipitation and
other parameters;
Managing the economic efficiency of
enterprises, taking into account the assessment of
disaster impacts on enterprises and environmental
situations on the population, of adaptation to
climate change.
The first two directions of applying DT elements
use existing data streams. Here, it is required to data
from various spheres of research (air, water, soil,
space). To do this can use the tools of integrating
data from different domains based, for example, on
the Unified System of Information on the Situation
in the World Ocean (ESIMO, http://esimo.ru).
The third field of application of DT, according
to the author, is the most important, promising and
significant in the face of climate change and an
increased number of disasters. To apply a DT in this
field, it is necessary to solve the following tasks - to
determine tools for its creation, to develop the
composition of the data, to find data sources for it,
and develop requirements for data models.
The creation of DT for the organization of
consumers HMS requires the use of a wider range
of data [7] - observed, prognostic, climatic
generalizations, information on the state of serviced
enterprises, reflecting the current economic, social,
technical, economic and organizational situations at
the enterprises. This will allow modeling possible
impacts on various enterprises, assessing possible
damage for different levels of danger, calculating
the cost of preventive actions and optimizing
business decision-making processes taking into
account all available information.
In hydrometeorology, models for analyzing and
predicting weather have been widely used for many
years. However, each discipline (meteorology,
oceanography, hydrology, ecology) uses its own
models and data. These models use many of the
same parameters. They must take from one source -
DT. An interesting tool in the development of this
area of DT use is the Logos software for
supercomputer modeling and engineering analysis
of the “Rosatom” State Corporation
(https://www.cnews.ru/articles/2021-11-
02_kak_razvivaetsya_inzhenernoe_po_v_rossii).
She includes the modeling of aero-, hydro- and gas-
dynamic processes, heat transfer, static and
dynamic strength, deformation and destruction in
industrial structures that occur under the impacts of
disasters. The software is used both in the nuclear
industry and in other industries: aviation, rocket and
space, oil and gas, shipbuilding and mechanical
engineering. An example of a topical problem
solved with the help of this software is the study of
the behavior of structural elements in permafrost. In
this software new functions are needed to simulate
icing (taking into account the operation of heaters),
the behavior of rockets (taking into account wind
loads, the behavior of systems when flying at up to
and supersonic speeds).
An interesting discussion of the creation of DTs
in the field of earth sciences has been presented in
the article [8]. Many questions like “Why is it
necessary to create a DT?” are in this paper. The
DT blocks and problems to be solved (data
integration, interoperability, scalability, reducing
the complexity of existing systems and services) are
considered.
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Evgenii Viazilov
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The purpose of this article is to study approaches
to creating DT in the field of using data of earth
sciences in the business processes of enterprises,
and developing requirements for it.
2 Methodology of digital twin’s
development in field of earth sciences
When creating a DT [9], it is necessary to consider
three options for reflecting the properties of the
environment that the consumer can use:
Digital shadow - reflects various properties of a
physical object obtained on the basis of observed
data at irregular measurement points;
Digital twin - allows, based on the values of the
properties of a physical object and models, to
restore the values of properties in regular spatial
grid by interpolation or extrapolation and then
predict condition in time;
Digital product is obtained based on the values
of the observed properties of the environment and a
physical object and or calculated, predicted values
based on models.
Properties reflecting the environment consider
more detail. Now observations are being made of
environment state and changes associated with
human activities with increasingly detailed spatial-
temporal scales of measurement. These
observations are recorded digitally on technical
media in the form of numerous files and DB. The
Earth is covered measurementы very unevenly and
they do not reflect with 100% completeness all the
properties of the Earth. Therefore, observations are
only a digital shadow of the Earth.
Integration of observational data on the
properties of the environment and its natural and
anthropogenic changes, as well as the use of models
of interpolation, extrapolation, assimilation of
various types of data in analysis and forecast
models, allows to create the digital twin of the
Earth or its individual objects (sea, federal subjects,
cities, enterprises, business process) and forecast
their behavior in time and space. Of course,
interpolation models to grid points are not yet
available for all parameters. Input and output
formats of data from models are not standardized
enough. Many models are based not on DB, but on
file systems. In addition, when restoring and
predicting the state of the environment, it is
necessary to take into account DTM, the type of
underlying surface, including artificial objects built
by man.
Based on the obtained reanalyses, time series,
operational analysis and forecasts, digital products
are being prepared that can already be used to solve
applied tasks using various models for optimizing
decisions at objects that depend on the state of the
environment; of assessment of impacts forecast of
disasters; of economic development planning, etc.
Unfortunately, still very few consumers are ready to
accept digital products in their business processes.
In the field of the environment, the creation of
new and improvement of existing models for the
analysis and forecast of atmosphere and
hydrosphere is ongoing. These models have been
widely used for over 60 years. To improve the
quality of forecast results, the spatial resolution of
analyzes and forecasts are increased. There are
already regional models with resolutions down to 3
km or less. Models hide complex mathematical
equations and different boundary conditions under
their shell. The work results of these models are
elements of the DT Earth. For example, the ERA5
reanalysis [10] includes more than 100 parameters.
The site [11] at https://earth.nullschool.net/ru
presents the “Virtual Earth” application for next
parameters – wave heights, direction and speed of
the current and the wind on the surface and heights,
distribution of aerosols, of dust. Data obtained from
the National Center for Environmental Predictions
and the US National Weather Service. This is
already good data for DT.
Any change in service, data flows and work
processes must teste on DT and then implemented.
With the help of this data, it is possible to solve not
only the operational tasks of preparing for a
disaster, but also the tasks of strategic planning on
enterprises of different industries. DT is a tool that
allows comprehensively reflecting the state of the
environment, the processes occurring in the
atmosphere (cyclones, anticyclones, fronts, etc.),
hydrosphere (currents) and lithosphere (thawing
permafrost). The models should allow describing
the behavior of natural objects in all situations,
including the impact of disasters and the behavior
of real economic objects during the period of such
disasters.
From the point of view of physical display, DT
is a data model that digitally reflects the properties
of real objects, based on which the following
functions can perform:
Providing a digital copy of the properties of
specific objects and or processes;
Display of objects and processes on the
computer screen in the form of isolines of
parameter values, areas of their extreme values,
atmospheric front’s lines, which helps to raise the
awareness of heads;
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Description of the current location, economic
state, operating conditions, behavior and other
properties of enterprises;
Management, aggregation, and analysis of data;
Modeling of a system for monitoring the state,
diagnostics of equipment and other elements of
enterprise management on which impact by a
disaster.
When using DT to predict disaster impacts on
various activities, on equipment and materials),
technical, economic and socio-economic
information about objects should be used. In this
case, the DT of the enterprise should be used, which
describes the real cause-and-effect relationships
between productions, economic, financial,
organizational indicators of the enterprise and
external impacts. Mathematical models of an
enterprise should also reflect the economic side of
its work; demonstrate how air temperature, wind
speed, precipitation can affect it. For example, wind
speed can increase or decrease the speed of a ship.
The more accurate the calculation of impacts
needed the more detailed data on the environment
in space and time will be required. In this case,
algorithms must take into account the vulnerability
of the object (wind resistance, strength, reliability,
survivability, corrosion resistance). Each of these
indicators depends on climatic parameters, overall
characteristics of the object, and the place of use.
DT is becoming a source of up-to-date data for
services and applications that will help solve every
day and future tasks for a wide range of consumers.
To implement such calculations, it is necessary:
Create unified rules for the formation of the data
composition, structure, types of storage and
designation of vulnerability indicators of
enterprises;
Develop algorithms and models for calculating
enterprise vulnerability indicators;
Prepare a digital formalized passport with
indicators for all disasters and determine the main
indicators of the object's vulnerability (mandatory,
recommendatory, general, sectoral, and
prospective).
The digital passport should reflect types of
activities, materials used, components,
manufactured products, and the production that is
affected by disasters. The prototypes of passports
for these objects already exist in the form of safety
passports for industrial enterprises, territories
(developed in accordance with Decree of the
Government of the Russian Federation of December
18, 2014 No. 1413), which considers impacts of
disaster on enterprises and their activities;
manufactured products, materials used to create
them. The development of such passports should
involve Roshydromet organizations that provide
HMS of enterprises, and experts from other
industries.
With the help of DT, it is possible to assess the
impacts of environmental changes on business
processes by modeling disaster impacts scenarios of
various danger levels on the activities of industrial
enterprises. Consumers of materials and
components should use digital passports with local
threshold values for disaster indicators in impacts
assessment models. The suppliers of materials and
components must remember that each manufacturer
lays down certain requirements for them for their
use, both in the construction of the enterprise and in
the production of products. It is also necessary to
take into account the life cycle of an enterprise,
manufacturing products from design to disposal.
Manufacturers write in the instructions that
accompany each product what should not be done
with the product, what side effects can happen to it
or to the enterprise, where the materials provided by
the manufacturer can be used and under what
external environmental conditions. Manufacturers
of materials, components, products must determine
in advance not only certain conditions (threshold
values of parameters) in which they must be
operated, but also what needs to be done to preserve
the properties of a particular product during storage,
transportation and use. This information will be the
part of DT, claiming to be the object of modeling
the impacts of the environment (Fig. 1).
It is important that experiments with DT can
carry out long before the disaster. The development
of DTs will help create new tools for predicting the
possible impacts of a disaster and assessing their
consequences. Such DT tools should be models of
impacts, damage assessment, calculation of the cost
of preventive measures, as well as modeling the
consequences of decision-making.
DT should describe not only all the properties of
the objects, but also their changes under certain
conditions. In other words, it intends, first, for
mathematical modeling of an object in order to
predict, for example, how the state of an object will
change during a disaster. DT is especially important
for the development of digital transformation and
the transition to Industry 4.0, allowing radically
changing business processes that take into account
the impacts of disasters.
To create a DT in the field of the environment, it
is necessary to develop end-to-end automatic
processing of data in the form of a pipeline “from
observation to decision making” [12]. At present,
this is possible so far only in special cases, when all
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indicators of the state of the environment are
monitored directly at the object. Given the needs of
consumers and the pace of automation, the solution
of this problem will be possible in the coming
years.
Fig. 1. Scheme of DT creation and using.
3 Composition of data for the digital
twin associated with the processes of
the impact of disasters on enterprises
From the point of view of users, DT is a DB, which
operates through a single user interface. To create it,
it is necessary to collect and analyze not only
environmental data, but also information about its
impact on various objects and their activities.
Together, all these data will make it possible to
predict the behavior of a real object under the
impacts of the environment.
DT based on research data and ancillary data
related to existing real systems from different
domains. Research results include data:
Experimental, collected with the active
participation of researchers;
Observed, collected by measuring the properties
of disasters and natural processes using contact and
remote devices with various sensors;
Simulated, created with the help of computer
models based on imitation of processes or systems
of the real world;
Compiled / derivatives created by transforming
and/or combining data already collected, including
various data sources;
Analyzes - the results of interpolation of
observational data in points of a regular grid with
different scales of space-time resolution;
Generalized, including climate data, obtained by
aggregating with different spatial-temporal
resolution observed data
Prognostic with different lead times - calculated
values of the properties of objects, possible in the
near or distant future;
Archival / documentary, created from existing
archival sources and / or documents in which
experimental or observed data published;
Historical data - includes, for example,
information about disaster indicating what happened
at the enterprise, when, what damage was, what was
done;
Formalized descriptions of indicators - local
threshold values of disaster parameters for levels of
danger and depending on the type of industrial
enterprise, type of activity.
Data
-Digital passports of enterprises
(territories)
-Environmental data
(operational, historical,
predictive, climatic, generalized)
-Technical and economic
-Social
-Economic
-Organizational
-Local threshold values of
disasters indicators
-Information about disasters
-……….
DT
-Data in regular grid points
-Time series of aggregated (daily,
monthly and annual
generalizations) with data on the
environment, feasibility studies, and
social, economic, organizational in
the form of time series
-High-resolution spatial data,
including DTM
Models for
analyzes and forecast
disasters
-Models of interpolation
and extrapolation in
time and space
-Forecast models
-Statistical models
-…….
Models for decisions support
-Evaluation of disasters impacts
on industrial enterprises and
activities
-Damage estimates
-Calculation of the cost of
preventive actions
-Calculation of consequences
of decision making
Heads
-Recommendations
-Installation
Tools
-MeteoAgent
-Dashboard
-MeteoMonitor
Information on
disasters
Tools of decisions
support
Results
-Improving the safety of the population
-Damage reduction
-Improving the efficiency of decision making
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For DT, in addition to the properties of the
environment, socio-economic, technological,
organizational and other information on objects that
may affect a disaster are added.
Organizational information includes information
for managing the HMS. These are information about
objects - detailed technical characteristics of objects
with indication of dimensions and other properties.
Economic information includes planned
performance indicators of enterprises, information
about damages; results of the functioning of objects.
Technological information includes, for example,
information about merchant ships approaching the
port and located under unloading, the movement of
export and import cargo in the port, etc.
Technical and economic information includes
danger indicators for objects (remoteness from the
water's edge, distance to the nearest shelter; critical
exposure time; year of construction; characteristics
of the terrain, soil; building density). Some of this
information is already included in the safety
passports of objects.
Social information includes place of residence,
place of work, crowded places (theaters, cinemas,
railway stations, and stores), etc. Need information
about the types of activities on which are affected by
the disasters, information on local roads, the
composition of the population, and technical
resources for cases of spills of oil, oil products and
toxic substances.
A key feature of DT is the availability of
enriched data with high-resolution in space and time.
When creating technical, economic and socio-
economic information, up to 90% of the data for
each object of the economy will be created and
remain unchanged. The transition to integrated data
will provide enriched data and additional slices of
data on the activities of the enterprise (indicators of
output, the number of employees, others);
information about the products manufactured by the
enterprise (average values).
For a full-fledged decision-making, in addition to
the entities listed above, a significant development
of information resources about the environment is
necessary, which are not currently being prepared,
for example, there are very few long-term
hydrometeorological forecasts. They are either
absent in existing information systems or presented
in a form not suitable (in the form of text) for
automatic analysis.
The most obvious way to populate DT is to get
the necessary data automatically from the outside
from physical systems (Internet of things, RFID,
mobile Internet devices); other departmental and
corporate IT systems using API and REST services.
Due to the need to take into account environmental
conditions, enterprises purchase their own automatic
stations and other devices, with the help of which
they control the minimum list of parameters
necessary to solve exclusively their tasks. The data
obtained are not always included in the state
observation network. Enterprises create their own
composition, structure, storage types, and data
attributes.
“DT serves as a framework for development and
training” in many applications [13].
4 Data storage requirements for digital
twin
Currently, data on the environmental and related
domains for DT are located in various sources, DB
and file systems. A large number of data sources,
each of which has its own formatting rules, a system
of local names, and classifiers with various
standardization levels, are one of the main
difficulties for data integration. When integrating
data from multiple sources, many details need to be
analyzed to present the data in a way that all
consumers can understand. Harmonization of
different formats and classifiers from different
sources, standardization of parameter names and
other information are necessary processes when
performing data integration. For the functioning of
DT, it is necessary to use the data collected and
calculated by models from the different agencies.
Additional data for DT in the field of earth sciences
arise at the stage of design, construction and
operation of enterprises, and, changing over time,
accompany throughout its entire life cycle.
The foundations for the creation and
development of DT are:
Data management at the object level, i.e. all data
about one object is stored in a single database;
Data integration - combining information
resources in one place for their further use;
Availability of metadata about sources and the
integrated data themselves;
Ensuring the compatibility of integrated data on
the unity of attribute names for the same entities, the
use of standardized classifiers;
Standardization of presentation of properties of
objects used in different DB (for example, location
coordinates - latitude, longitude, time - date and
other key object attributes must have the same
names);
Use of a unified system of classifiers and codes
(local, departmental classifiers should be brought to
a higher level of standardization - national or
international);
Linking data for different DT objects the
presence of links for joint consideration of different
data.
All the necessary information to create DT
objects must be integrated and always up to date.
The scheme of data integration, creation of DT and
its application in various services has been shown in
Fig.2.
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.6
Evgenii Viazilov
E-ISSN: 2945-0454
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Fig. 2. Scheme of data integration, creation of DT and its application [14].
It needs to create a metadata catalog containing
contextual information about the data the consumer
is going to access, such as where the data came
from, who created it, and when it was last updated.
Metadata change as additional data sources are
connected, algorithms are configured and
modified. The metadata catalog provides a
semantic layer that represents each object (person,
organization, impact process, material, and
product) and their relationships to metadata
objects. This will help consumers get more
relevant and faster responses to queries.
For the manufacture of DT, data from automatic
stations operating in real time should be used. The
data must be up-to-date and updated in accordance
with the regulations. Each data set should include
fixed properties - metadata, dynamic - life cycle of
object, links with other data sets.
Data presented in the form of analyzes and
forecasts must meet the following requirements -
to be issued with a time resolution of up to 3 hours,
and in some cases up to 10 minutes. The following
requirements apply to data that is additionally
involved in DT:
Information on the possible impacts of disasters
on various enterprises, activities, raw materials,
products and people should be formalized;
Information should be of high quality and
detailed - from urgent observations to climate
generalizations;
Information must be in digital form.
Passports of enterprises and other information
should be formalized, digitized, normalized and
integrated. In fact, integrated data should be
created, both for points and in regions.
All data must be managed from the moment it
enters to the DT to the moment it is used.
Heterogeneous data are integrated, processed and
used to create new systems (modeling, forecasting,
and decision support). DT should be created in
such a way that any application can get the
necessary slice of data without human intervention.
It is important that DT reflects the emerging
situations at an industrial facility, that is, the data
must be constantly updated, checked for
correctness and be available in real time.
DT must also comply with the FAIR principles
(findability, accessibility, compatibility, reuse)
[15], the repository for DT must satisfy the
TRUST criteria (transparency, responsibility, user
orientation, sustainability, manufacturability) [16]
and CARE (collective benefit, authority on control,
responsibility, ethics) [17].
An equally important requirement for DT is the
development of the new data model for it. At this
stage of research, it is too early to talk about a
specific data model, but even now, it can be argued
that the main requirement for such a model is the
following.
The storage unit of the DT is the properties of
one instance of the object. Each type of DT has
own database. The DT model must include all the
required objects. All object properties must be
stored in one table [18].
DT must have a consistent set of services,
search capabilities, and data accessibility that will
enable consumers to obtain the required digital
production to solve an application task.
To create either a global, or national, regional,
or local DT, it is necessary to develop a Data
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Management Plan, which reflects the data sources
for the formation of DT, the rules for their
replenishment, as well as the rules for its use in
solving various tasks. The latter is necessary to
increase the level of automation of its creation and
use it as a continuous data processing pipeline
from observation to decision-making [12].
5 Discussion
Data integration will make it possible to convert all
data into a common format and visualize it in a
uniform way or deliver it to consumers by
subscription by REST and other services. The
resulting operational data allows consumers to
identify disasters based on local thresholds.
Namely at these disasters is a necessity of support
of decisions. This requires the use of not only data
of earth sciences, but also additional information
about the socio-economic objects, various
enterprises, that depend on disasters. To do this,
data from different domains must be in the DT.
Thus, a single data source will be created for its
subsequent use in various tasks of forecasting and
modeling. Based on DT, it will be possible to
develop applications to take into account impacts
of disasters on the activities of enterprises and the
population. Consumers will be able to receive up-
to-date data at any time, for any region and in the
required composition of attributes, on any Internet
device, wherever the consumer is.
Digital products obtained with the help of the
DT are as follows:
Operational continuously updated observational
and forecast data;
High resolution data with higher update
frequency;
Forecasts of disasters;
Climate generalizations;
Data from various sectors of the economy
(social, economic, technical, etc.);
Impacts of disasters on businesses and the
public.
The performance indicators of DT are
following:
Reliability of DT infrastructure operation - not
less than 99.9%;
Failure time should not exceed 5 minutes;
Response time to the actions of the head of the
enterprise using DT tools - 5 s;
Relevance of data – data should receive from
hydrometeorological stations with a delay of no
more than 30 minutes;
Time of collection and processing should not
exceed 40 minutes for operational data and two
hours for the forecast of the main meteorological
parameters after their measurement;
Measurement of disaster indicators should be
carried out as often as possible, but not less than
once every three hours during normal conditions
and more frequent observations (every 10 minutes)
when the disasters occurs;
Time for delivering to consumer’s information
about a disaster should not exceed 15 minutes after
it received or identified;
Time for obtaining additional information about
the current situation should not exceed three
minutes after uploading to the DB.
DTs in the field of earth sciences are not
something completely new. This is a transition to a
new level of abstraction (work with databases at
the objects level); an increase in the level of
automation of data processing; an expansion of the
data sets required to solve the business processes
of an enterprise. Each data source can no longer
fully satisfy the information needs of enterprises,
preliminary coordination of data from various
sources is required in terms of both standardizing
data structures, applied attribute names, units of
measurement, used classifiers, and organizing fast
delivery of update data. These tasks will be
performed by DTs.
DTs should become components of smart cities,
digital enterprises, self-driving cars, drones, ships
and other objects that require data on the state of
the environment. This is especially important in
terms of predicting possible disasters impacts on
the population, enterprises, and protecting the
environment from the harmful effects of
enterprises. DTs will allow quickly identifying the
causes of equipment failures related to disaster
impacts. DT can be used to model disaster impacts
on industrial plants as well as for green promotion
[19].
The development of a DT in the field of earth
sciences will require a lot of time for
understanding the problem, implementation and
widespread use in industrial enterprises by all
participants in the HMS processes.
6 Conclusions
For the first time, approaches were presented to
create a DT in the field of earth sciences, which
can be used at the global, regional or local levels.
An exemplary composition of DT data for HMS of
consumers proposed; requirements for DT
developed.
DT is a tool for creating a DB for many objects
and his further application in various computer
models. DT is a shared data space ecosystem in the
domain of earth sciences, social and economic
objects interacting with the environment. DT
implies the presence of metadata (information
about it, including the creators, what, when it was
changed), compliance with the FAIR principles,
TRUST and CARE requirements. Each DT should
have a unique identifier and name, associated
International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.6
Evgenii Viazilov
E-ISSN: 2945-0454
49
metadata to make them easier to use. Metadata
should reflect both the content of the DT and
information about data sources (organizations,
observation platforms, instruments, methods for
obtaining and processing parameters).
Thus, the creation of a DT will allow any
external system to obtain the necessary data slice
without human intervention. It is important that DT
will reflect the emerging situations at enterprises,
that is, the data will be constantly updated, checked
for correctness and available in real time. This
allows monitoring deviations and instantly
determining the level of danger.
DTs can use to solve such tasks as, for
example:
Assessment of possible damage in the event of
a disaster, calculation of preventive actions cost an
optimization decision support;
Optimization of fuel consumption (coal, gas,
electricity) depending on the season, short-term or
long-term forecast of weather conditions;
Taking into account the actual values of wind
indicators (speed and direction) and other
parameters on the runway;
Operational adjustment of the route of delivery
of electronic equipment, taking into account the
weather forecast or the mode of operation of the
refrigeration unit when transporting perishable
products.
DTs of enterprises will allow modeling the
impact of disasters on certain objects at various
stages of their life cycle. Creating a DTs based on
integrated data allows saving on data entry into
information systems of enterprises; improve data
quality by removing the human factor; and
increases the safety of objects exposed to disaster
impacts. At the same time, it will be possible to use
modern modeling methods using machine learning,
neural networks, which will allow us to study
physical objects in more detail. For example, it is
possible to detect anomalous situations, both in the
environment and at enterprises, in equipment,
which can lead to serious problems during a
disaster. DTs will also allow refinement of danger
levels based on local thresholds for each enterprise
and activity. DT will provide various interfaces in
the form of dashboards, Weather Monitor for the
operator and decision support. For business heads,
these are recommendations for managing the
enterprise.
Further research in the field of creating DTs in
the earth sciences, in the opinion of the author,
should be aimed at developing models for
assessing impacts of environment on enterprises,
calculating possible damage before the onset of an
disasters, the cost of preventive actions, and the
optimization of decision-making based on
simulation results.
DT will develop in the framework of the “The
Caspian Sea Digital Twin” Programme performed
as part of the IOC activities related to the UN
Decade of Ocean Science for Sustainable
Development. The Caspian Sea DT would include
an updated archive of satellite, oceanographic,
hydrometeorological, hydrodynamic model;
atmospheric reanalysis data, results of regional
climate change forecasts, electronic atlases, and
electronic library of publications on the Caspian
Sea. The DT database will allow assessment of
anthropogenic loads on the Caspian Sea
environment, ongoing climate change, extreme
weather and climate events, the impact of climate
change on natural and socio-economic systems,
development of a strategy and mechanisms for
adaptation to climate change and the state of the
Caspian Sea.
Getting data from the DT will lead enterprise
heads to partners from hydrometeorological
organizations for the development of new
products, services, and the accumulation of
experience in data processing that cannot be
implemented independently. Thanks to digital
transformation and DTs, consumers will think
about business development, and not solve the
technical issues of IT development
Acknowledgments:
The research was conducted in the framework of
the “The Caspian Sea Digital Twin” Programme
performed as part of the IOC activities related to
the UN Decade of Ocean Science for Sustainable
Development (2021-2030).
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International Journal of Applied Sciences & Development
DOI: 10.37394/232029.2022.1.6
Evgenii Viazilov
E-ISSN: 2945-0454
51