Small Disaster Prevention: Edge AI for Wildlife and Flood Management

As climate change accelerates, extreme weather events such as wildfires and floods are becoming more frequent and severe. The increasing unpredictability of these disasters puts immense pressure on emergency response teams, infrastructure, and communities. Traditional monitoring and prediction methods often fail to provide timely or accurate alerts, leading to devastating consequences.

To address these challenges, organisations responsible for public safety are turning to advanced AI and IoT-based solutions that offer real-time data processing, predictive analytics, and automated alerts. These technologies enable authorities to detect threats earlier, respond faster, and minimise environmental and economic damage.

One such organisation, facing escalating risks from wildfires and flash floods, required a rugged and reliable computing solution capable of operating in extreme conditions. The client sought an intelligent, real-time wildfire detection and flood monitoring system to enhance situational awareness, automate decision-making, and improve emergency response strategies. By leveraging Edge AI-powered computing systems, they aimed to strengthen disaster preparedness, reduce false alarms, and increase response efficiency.

The client wished to deploy IoT sensors, AI-driven analytics, and rugged edge computing to revolutionise their disaster monitoring and mitigation efforts.

Wildfires spread rapidly, making early detection critical. Traditional methods like satellite imaging and manual observation can be slow and ineffective, especially in remote regions. The client needed a rugged, real-time monitoring solution to detect early wildfire indicators and enable rapid response.

Challenges in Wildfire Detection

• Delayed detection due to reliance on human observation
• Inaccessibility of remote, high-risk wildfire zones
• Need for real-time environmental data to trigger early warning systems

Frequent flooding is also a growing concern, and the client required a high-speed, real-time monitoring system capable of predicting and responding to flood risks before they escalated into disasters.

Challenges in Flood Monitoring

• Unpredictable weather patterns leading to sudden flash floods
• Lack of real-time monitoring systems in high-risk areas
• Need for fast and accurate data processing to ensure timely alerts

To tackle the challenge of wildfire detection, the client implemented Axiomtek’s ICO300-83M, a rugged DIN-rail edge gateway engineered for real-time data collection and processing in hazardous environments.

Key Features & Benefits

• Certified for Hazardous Areas: ATEX & CID2 certified for safe operation in volatile conditions.
• Compact & Power-Efficient: Utilises an Intel Atom® processor with ultra-low power consumption (<10W), ideal for remote deployment.
• Wide Operating Range: Functions in extreme temperatures (-40°C to +75°C), with a 9-36V DC power input and 3 Grms vibration resistance.
• Rich Connectivity: Features USB, LAN, HDMI, and isolated COM ports, enabling seamless sensor integration.
• Advanced Software Support: DigiHub and eAPI streamline integration, accelerating deployment.

Implementation

The client deployed IoT sensors and cameras in wildfire-prone regions to track temperature, humidity, wind speed, and smoke levels. The ICO300-83M processed this data in real time and transmitted alerts to the monitoring centre. Using AI-driven analytics, authorities were able to predict fire behaviour, issue early warnings, and coordinate rapid firefighting responses, reducing damage and saving lives.

To meet the challenge of Real-Time Flood Monitoring, the client deployed Axiomtek’s eBOX671B, a powerful edge AI computing system designed for high-speed flood forecasting and monitoring.

Key Features & Benefits

• High-Performance AI Processing: Powered by 14th/13th/12th Gen Intel® Core™ i9/i7/i5/i3 or Celeron® processors, ensuring rapid data analysis.
• AI-Enhanced Flood Prediction: Supports MXM 3.1 Type A GPU cards, enabling real-time AI-driven flood forecasting models.
• Robust Data Storage: NVMe M.2 2280 & dual 2.5″ SATA HDD/SSD with RAID 0,1 for secure, high-speed data access.
• Multi-Display Monitoring: Supports up to five simultaneous displays, improving situational awareness in control centres.
• Reliable & Rugged: IP40-rated, withstands extreme temperatures (-40°C to +65°C), 3 Grms vibration resistance, and 9-36V DC power input.

Implementation

The client installed IoT sensors along rivers and flood-prone areas to track water levels, rainfall, and soil moisture. The eBOX671B analysed this data in real time and used AI algorithms to predict potential flooding. The system relayed alerts to emergency services, enabling timely evacuations and infrastructure protection. By integrating AI with real-time IoT data, the client significantly improved flood preparedness and response times.

By implementing Axiomtek’s rugged edge computing solutions, the client successfully enhanced real-time wildfire detection and flood forecasting capabilities. These Edge AI-powered systems provide 24/7 environmental monitoring, predictive analytics, and rapid emergency response—helping authorities mitigate disasters and protect communities.

With the power of IoT-driven insights and AI-enhanced decision-making, organisations can proactively address climate-related risks, ensuring a safer and more resilient future.