Our Solutions
Forest Fire Detection
This solution introduces a system that merges AI with low-power IoT (Internet Of Things) technology to enhance environmental monitoring, with a specific focus on accurately predicting forest fires through time series analysis. One of the key features of this approach is the comparison of the real-time local environmental data with meteorological service environmental data to ensure accuracy. Finally, the decision tree model serves as the last step, providing a comprehensive assessment of fire risk due to its straightforward application and clarity. Validation of the fire detection component remains a critical future task to confirm its effectiveness and reliability.
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Forest Fire Detection
01
Sensors
Sensor nodes are measuring temperature, humidity, and air pressure values, continuously monitoring these environmental parameters to provide real-time data.
02
Cloud & Data Collection
The data packages are being sent to The Things Network via LoRa (Long Range IoT), ensuring efficient and long-distance transmission of information. This method utilizes LoRa's low-power, wide-area network capabilities to reliably deliver environmental data from remote sensor nodes to the network for further processing and analysis.
03
Analysis
Conducting reliability checks and identifying anomalies in time series data to ensure the accuracy and consistency of the collected information.
04
Fire Risk Alert
Sending an alert if an anomaly is detected, notifying relevant authorities to enable immediate action and potentially prevent the escalation of a forest fire.