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Spatial systems
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<a id="navigation-link" href="/emergency.html">РУ</a>
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<div class="fullpage">
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<div class="section gradient-text">
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<p>Heat Emergency ————— a geoinformation ML system devoted to modeling of emergencies at heat supply
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facilities.
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</p>
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<p>Every day the city accumulates data on the emergencies that have occurred at heat supply facilities -
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residential buildings and heating points. A huge array of such data, combined with data on the
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characteristics of heat supply facilities and weather conditions, allows you to create new emergency
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management tools.</p>
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<p>
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HeatEmergency is an application for the city that helps not only improve control to prevent
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emergencies,
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but also build the best response scenario to an accident that has already occurred
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</p>
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</div>
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<div class="section">
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<figure>
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<img src="/images/emergency/cover.jpg" />
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<figcaption>System interface</figcaption>
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</figure>
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</div>
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<div class="section">
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<p>Before using, it is necessary to import all the necessary data into the system — locations of residential
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buildings and infrastructure facilities, communication of heat supply facilities with each other,
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emergency situations of different types that have occurred in residential buildings, indicators of
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general house metering devices</p>
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<p>In case of industrial use of the system, all data should be imported in advance, and data requiring
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frequent updates (emergency situations and indicators of general house metering devices) should be
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transmitted to the system automatically through integrations with the corresponding information systems
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of housing and communal services.</p>
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</div>
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<div class="section">
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<figure>
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<img src="/images/emergency/import.jpg" />
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<figcaption>Import of source data</figcaption>
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</figure>
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</div>
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<div class="section">
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<p>After importing the emergency situations data into the system, a training set is formed, consisting of
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the emergencies of a certain type in certain residential buildings on specific dates and the data of the
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absence of such accidents.
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</p>
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<p>Besides, many signs are formed for the model, including the type of walls, year of construction, number
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of storeys, data on the overhaul of various parts of the water supply system of residential buildings,
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etc. (about 50 signs in total), weather conditions.</p>
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<p>When building a model, the problem of binary classification is solved — the model determines the
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propensity of each residential building to different types of accidents (for example, leakage in a pipe
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in an apartment or entrance, lack of heating, etc.).</p>
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<p>The result of the predicted propensity of residential buildings is displayed on the map with the
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possibility of using many filters.</p>
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</div>
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<div class="section">
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<p>After importing indicators of general house metering devices, the metrics on the pressure and water
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temperature in the pipe at the entrance to the house are compared with the standard ones. In case of
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deviations from the standards, the fact of an accident on a source — heating point is registered.
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Further, similarly to emergencies in residential buildings, an ML model is built, which determines the
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propensity of the heating point to an emergency.
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</p>
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<p>In forecasting, we use complex models that allow us to interpret the propensity for emergencies in a way
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that is clear to the user — the forecast can be decomposed into contribution level of all factors
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involved in the model and better understand which factors are most significant.</p>
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</div>
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<div class="section">
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<figure>
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<img src="/images/emergency/shap.jpg" />
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<figcaption>Propensity prediction interpretation — visualization of the contribution of various factors
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to the prediction of the propensity of a residential building the occurrence of a leak in the
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heating system
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</figcaption>
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</figure>
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</div>
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<div class="section">
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<p>In the system, it is possible to create a simulation of an accident that has already occurred at a
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heating point to assess the number of consumers (residential buildings and socio-economic facilities)
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who find themselves in the accident zone and the rate of their cooling down, in order to form an optimal
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sequence of response and levelling of the emergency situation.
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</p>
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</div>
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<div class="section">
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<figure>
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<img src="/images/emergency/sim.jpg" />
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<figcaption>Simulation of an emergency at a heating point
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</figcaption>
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</figure>
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</div>
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<div class="section">
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<p class="gradient-text">The functionality of the HeatEmergency system based on data analysis and machine
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learning methods allows you to improve control over the city heat supply system, improve understanding
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of patterns in the occurrence of emergency situations and prevent them.
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</p>
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<a href="/en/contacts.html" class="contacts-link">Contact us if you are interested in a system like
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