kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2025-22-4-254-265kaz29-2298Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEСИСТЕМНАЯ АРХИТЕКТУРА ДЛЯ СИСТЕМЫ УПРАВЛЕНИЯ СТИХИЙНЫМИ БЕДСТВИЯМИA SYSTEM ARCHITECTURE FOR DISASTER MANAGEMENT SYSTEMhttps://orcid.org/0009-0000-7727-9095KайдуллаевМ. А.KaidullayevM. A.

докторант

г. Алматы

PhD student

Almaty

mi_kaidullayev@kbtu.kzAкжаловаА. Ж.AkzhalovaA. Z.

PhD, профессор

г. Алматы

PhD, Professor

Almaty

a.akzhalova@kbtu.kzКазахстанско-Британский технический университетКазахстанKazakh-British Technical UniversityKazakhstan202523122025224254265Copyright © Kайдуллаев М.А., Aкжалова А.Ж., 20252025Kайдуллаев М.А., Aкжалова А.Ж.Kaidullayev M.A., Akzhalova A.Z.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2298

В эпоху ускоряющегося изменения климата и роста городского населения частота и интенсивность стихийных бедствий значительно возросли, представляя серьезную угрозу инфраструктуре, экономической стабильности и жизни людей. Такие стихийные бедствия, как землетрясения, наводнения и ураганы, часто приводят к обширным разрушениям зданий, требуя быстрой и точной оценки для экстренного реагирования и распределения ресурсов. В ответ на эти вызовы в данной работе представлена модель оценки ущерба зданиям на основе глубокого обучения, использующая гибридную архитектуру, сочетающую искусственный интеллект и интернет вещей. Данное исследование объединяет интернет вещей и искусственный интеллект для повышения автоматизации, прозрачности и устойчивости интеллектуальных систем. Система предназначена для сбора и анализа аэрофотоснимков до и после стихийных бедствий для классификации зданий по категориям ущерба – от неповрежденных до разрушенных. Кроме того, мы интегрируем модель в более широкую систему управления стихийными бедствиями, которая визуализирует оценки ущерба в геопространственном интерфейсе, позволяя лицам, принимающим решения, определять приоритетность пострадавших районов и оптимизировать меры реагирования на стихийные бедствия. Эта система призвана помочь государственным учреждениям, неправительственным организациям и службам быстрого реагирования принимать обоснованные и своевременные решения в ситуациях после стихийных бедствий.

In the era of accelerating climate change and growing urban populations, the frequency and severity of natural disasters have increased significantly, posing substantial threats to infrastructure, economic stability, and human lives. Disasters, including the likes of earthquakes, floods, and hurricanes usually are the reasons for serious destruction of buildings, requiring rapid and accurate assessment to aid in emergency response and resource allocation. In light of this, the research aims to deliver a deep learning based building damage assessment model, which is a hybrid architecture consisting of Artificial Intelligence and IOT. In this paper we will examine the use of Internet of Things (IoT) and Artificial Intelligence in disaster management systems in order to improve the automation, transparency, and sustainability in smart intelligence systems. The system should collect and analyze pre-disaster and post-disaster aerial imagery to classify buildings into damage categories, i.e. from no damage to destroyed.. Also, we integrate our model into a wide disaster management system in order to make a visualization of damages on a geospatial interface, that helps the decision-makers to get a quick look at priority areas and streamline the response of disaster. This system’s plan is to assist public authorities, NGOs, and first responders with quick decision making in postdisaster response times.

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The authors declare that there are no conflicts of interest present.