AI-Powered 6G-Based Real-Time Flood Prediction and Disaster Management System
The integration of 6G networks with environmental sensors and smart city infrastructure using edge AI for real-time flood prediction addresses the limitations of existing systems, enabling early detection and effective disaster management through digital twin-based risk maps and low-latency alarms.
Patent Information
- Application Number
- TR202617960
- Authority / Receiving Office
- TR · TR
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2026-10-15
- Publication Date
- 2026-10-21
AI Technical Summary
Current flood prediction systems relying on meteorological data and satellite information are inadequate in addressing real-time signal changes, user activity densities, and urban IoT infrastructure dynamics, leading to delayed flood warnings and ineffective disaster management due to insufficient integration of 6G network data and centralized cloud-based analysis.
An AI-powered system integrating 6G mobile communication networks with environmental sensors and smart city infrastructure for real-time flood prediction, utilizing edge AI for low-latency decision-making and dynamic traffic management, and generating digital twin-based risk maps.
Enables early detection of flood risks, dynamic traffic redirection, and timely emergency responses by integrating 6G networks with environmental sensors and smart city infrastructure, reducing loss of life and property through real-time flood prediction and low-latency alarm systems.
Smart Images
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Abstract
Description
1 TARIFF AI-Powered 6G-Based Real-Time Flood Prediction and Disaster Management The system Technical Area The invention is relevant to 6G mobile communication networks, smart city infrastructures, disaster management systems, and flood control. and flood early warning platforms, AI-powered environmental analysis systems, digital twin It can be used in the fields of (digital twin) applications and IoT-based environmental monitoring systems. By integrating 6G mobile communication networks with environmental disaster analysis systems, we can achieve a true 10. with an AI-powered disaster management system that performs timely flood and inundation predictions It is related. State of the Art Today, the risks of heavy rainfall, flooding, and flash floods are assessed using 6G network data, meteorological information, and 15... Real-time prediction using IoT sensor outputs and AI-powered network analytics. This is the goal. Additionally, the system addresses base station signal attenuation and user density (UE). (intensity), vehicle movement intensity, drainage sensors, rainfall amount, traffic density, Real-time flood risk is determined by analyzing sewer load and edge sensor data together. It functions as an advanced disaster management platform capable of generating maps. Thanks to this structure; 20 an integrated early warning system across both mobile communication networks and city infrastructures The aim is to reduce loss of life and property by establishing a decision-making mechanism. Current flood and inundation prediction systems rely largely on meteorological radars, rainfall sensors, and It is based on satellite data. However, these systems are affected by problems occurring in mobile communication networks. real-time signal changes, user activity densities, base station 25 performance changes, vehicle telemetry data and urban IoT infrastructures are not adequately understood. It does not take this into account. Similarly, existing disaster management systems are mostly centralized. It operates on cloud infrastructures and utilizes low-latency edge analysis mechanisms and real-world applications. They are unable to effectively utilize real-time telecommunications data. Mobile networks, on the other hand, are currently unable to do so. In this case, by actively analyzing environmental disaster risks, city management systems will address 30 It does not work in an integrated manner. Due to these shortcomings, sudden floods cannot be predicted early enough. It is not possible to do so, alarms may be delayed in critical areas, and traffic redirects are not possible. There may be delays, communication infrastructure can be overloaded during a disaster, and city infrastructure and mobile networks may be affected. Coordination between networks is not possible; local disaster risks are analyzed in real time. It is not possible. Especially in high-frequency 6G communication bands, heavy rainfall is occurring. 35 Signal attenuations caused by this are not accepted as meteorological data in current systems. 2 This cannot be evaluated. Therefore, the current technique; disaster prediction with 6G network data. Real-time and AI-powered integration between systems. It is unable to provide this. In conclusion, due to the negative aspects described above and the current solutions being the subject of discussion... Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. 5 The purpose of the invention The invention represents a new breakthrough in this field, unlike the structures used in existing technology. The aim is to create a structure with different technical specifications that bring these elements together. The main purpose of the invention is to integrate 6G mobile communication networks with environmental disaster analysis systems. an AI-powered system that performs real-time flood and inundation predictions by integrating them The goal is to create a platform. Another purpose of the invention is to provide users with signal attenuation data obtained from base stations. density, vehicle movement information, traffic density, meteorological sensor outputs, sewage Early detection of flash flood risks by analyzing sensors and IoT infrastructure data together. 15 to create real-time risk maps, determine regional disaster intensity, traffic dynamically directing the flow, optimizing emergency teams, city infrastructure to protect, send early warnings to mobile users, AI-powered disaster prediction The goal is to implement and provide a low-latency alarm system with Edge AI. Another aim of the invention is to create 20 digital twin platforms that can integrate with smart city infrastructures. data provider, disaster response communication prioritizer, emergency network slice capable of supporting disaster management, enhancing the disaster management capacity of mobile operators, and 6G The goal is to create a system that can adapt to the smart city architectures of the future. To achieve the purposes described above, the invention is related to 6G mobile communication networks. Real-time flood and flash flood prediction by integrating environmental sensor infrastructures 25 It is an AI-powered disaster management system that measures rainfall amount, humidity, IoT is a sensor infrastructure that generates water level, sewage load, wind and environmental data. meteorological sensors, signal attenuation, mmWave / THz attenuation, user density, and 6G is the communication infrastructure that transmits traffic changes to the system for real-time analysis. base stations, network KPIs, user density and environmental network data 30 6G core network and NWDAF, which are core network components that provide centralized analysis. The platform analyzes network data and meteorological data together to predict flood risk. AI-powered flood analysis module, which is an AI-based analysis module, Flood risk zones and disaster intensity maps on a digital twin of the city's infrastructure. Digital twin and risk mapping engine, which are simulation engines that create real-time disaster 35 3 Edge computing that provides alarm, traffic routing and low-latency decision-making. It includes an edge AI disaster management server with infrastructure. The structural and characteristic features and all the advantages of the invention are given in the figures below. The detailed explanation, written with references to figures, makes it clearer. This will be understood. Therefore, the evaluation should also take these figures and detailed explanations into account. 5 It must be done by taking it. Ways to Help Understand the Discovery Figure 1 shows the general architecture of the system that is the subject of the invention. Description of Part References 10 1. IoT and meteorological sensors 2.6G base stations 3.6G core network and NWDAF platform 4. AI-powered flood analysis module 5. Digital twin and risk mapping engine 15 6. Edge AI disaster management server Detailed Description of Find In this detailed explanation, the preferred configurations of the invention are not merely for better understanding the subject. It is intended to facilitate understanding and will not create any limiting effects. The invention; 6G mobile 20 real-time flood detection by integrating communication networks with environmental sensor infrastructures. It is an AI-powered disaster management system that performs flood predictions. The system includes IoT and meteorological sensors (1), 6G base stations (2), 6G core network and NWDAF platform (3), AI-powered flood analysis module (4), digital twin and risk It consists of the mapping engine (5) and edge AI disaster management server (6) components. 25 Under normal operating conditions, IoT sensors (1) measure rainfall, humidity, water level, and sewage. transmits payload and environmental data to the system. 6G base stations (2); mmWave / THz signal weaknesses, user density, traffic variations, and network performance parameters It analyzes and transmits to the core network. 6G core network and NWDAF platform (3); User density (UE density), cell load, network performance, latency, and traffic 30 It centrally analyzes the changes. AI-powered flood analysis module (4); Flood risk prediction by processing meteorological sensor data and grid KPI data together. It creates a digital twin and risk mapping engine (5); on the digital model of the city infrastructure. It creates flood risk zones and water flow simulations. Edge AI disaster management server (6); by generating low-latency disaster alerts, it can send them to mobile users, traffic management centers and 35 It sends real-time notifications to emergency teams. 4 In this way, the system makes real-time flood predictions and uses network data for environmental analysis. Digital twin-based risk maps that utilize AI-powered disaster management. an intelligent disaster management platform that creates low-latency alarms using Edge AI. It constitutes.
Claims
REQUESTS By integrating 1.6G mobile communication networks with environmental sensor infrastructures, real An AI-powered disaster management system that performs timely flood and inundation predictions. Its characteristic is; Sensor 5 generates data on rainfall amount, humidity, water level, sewage load, wind, and environmental data. IoT and meteorological sensors with infrastructure (1), signal attenuation, mmWave / THz attenuation, user density, and traffic variations 6G base stations (2), which are the communication infrastructure that transmits to the system for real-time analysis. Centralized analysis of network KPIs, user density, and environmental network data. 6G core network and NWDAF platform (3), which are core network components providing 10 It creates flood risk forecasts by analyzing grid data and meteorological data together. AI-powered flood analysis module (4), which is an AI-based analysis module Flood risk zones and disaster intensity maps on a digital twin of the city's infrastructure. Digital twin and risk mapping engine (5), which is the simulation engine that creates real-time disaster alert, traffic routing, and low-latency decision-making mechanisms 15 Edge AI disaster management server (6) which provides edge computing infrastructure It includes.
2. It is a disaster management system in accordance with Request 1, and its feature is the signal generated in 6G base stations (2). a processing unit that enables the assessment of their weakening for environmental analysis purposes It includes. 20 3. It is a disaster management system compliant with System 1, characterized by its user density and vehicle movement. It includes a processing unit that enables the data to be used in disaster risk analysis.
4. It is a disaster management system compliant with System 1, characterized by its integration of network KPIs and meteorological data. It includes a processing unit that enables them to be analyzed together.
5. It is a disaster management system compliant with Claim 1, and its feature is; AI-assisted flood prediction. 25 It contains a processing unit that enables its creation.
6. It is a disaster management system compliant with Claim 1, and its feature is a risk map on a digital twin. It contains a processing unit that enables its creation.
7. It is a disaster management system compliant with Claim 1, characterized by its low-latency disaster response via edge infrastructure. It includes a processing unit that enables the creation of an alarm. 30 8. It is a disaster management system compliant with Claim 1, and its feature is that it provides real-time early warnings to mobile users. It includes a processing unit that enables warning and traffic redirection.