Wildfire Early Warning System Based on a Smart CO<sub>2</sub> Sensors Network
Climate change exacerbates wildfire risks in regions like the Mediterranean, where rising temperatures and prolonged droughts create ideal fire conditions. Adapting to this scenario requires implementing advanced risk management strategies that leverage cutting-edge technologies. Wildfire early warn...
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2012 |
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| author | Alessio De Rango Luca Furnari Fabio Cortale Alfonso Senatore Giuseppe Mendicino |
| author_facet | Alessio De Rango Luca Furnari Fabio Cortale Alfonso Senatore Giuseppe Mendicino |
| author_sort | Alessio De Rango |
| collection | DOAJ |
| description | Climate change exacerbates wildfire risks in regions like the Mediterranean, where rising temperatures and prolonged droughts create ideal fire conditions. Adapting to this scenario requires implementing advanced risk management strategies that leverage cutting-edge technologies. Wildfire early warning systems are crucial tools for detecting fires at an early stage, helping prevent potential future damage. This paper proposes a smart CO<sub>2</sub> sensor network-based early warning system, relying on a platform that enables the connection, management, and processing of data from the devices through the cloud. The wildfire early warning system was tested in a real controlled experiment, in which 44 sensors were deployed in strategically selected locations at varying distances from the fire. To enhance early detection, three Artificial Intelligence (AI) models were developed using AutoEncoders (AEs) and Long-Short-Term Memory (LSTM), and these were compared to a simple threshold-based (NO-AI) model. All AI models, especially the LSTM-based model, were able to extract more valuable information from the CO<sub>2</sub> records, activating up to 56% more sensors than the NO-AI model in less time and tracking potential fire front propagation based on wind patterns. Therefore, the system not only improves early fire detection models but also effectively supports firefighting operations. |
| format | Article |
| id | doaj-art-91fa4d29d94647caba05063fccb8be9a |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-91fa4d29d94647caba05063fccb8be9a2025-08-20T02:15:46ZengMDPI AGSensors1424-82202025-03-01257201210.3390/s25072012Wildfire Early Warning System Based on a Smart CO<sub>2</sub> Sensors NetworkAlessio De Rango0Luca Furnari1Fabio Cortale2Alfonso Senatore3Giuseppe Mendicino4Department of Environmental Engineering, University of Calabria, Rende, 87036 Cosenza, ItalyDepartment of Environmental Engineering, University of Calabria, Rende, 87036 Cosenza, ItalyDepartment of Environmental Engineering, University of Calabria, Rende, 87036 Cosenza, ItalyDepartment of Environmental Engineering, University of Calabria, Rende, 87036 Cosenza, ItalyDepartment of Environmental Engineering, University of Calabria, Rende, 87036 Cosenza, ItalyClimate change exacerbates wildfire risks in regions like the Mediterranean, where rising temperatures and prolonged droughts create ideal fire conditions. Adapting to this scenario requires implementing advanced risk management strategies that leverage cutting-edge technologies. Wildfire early warning systems are crucial tools for detecting fires at an early stage, helping prevent potential future damage. This paper proposes a smart CO<sub>2</sub> sensor network-based early warning system, relying on a platform that enables the connection, management, and processing of data from the devices through the cloud. The wildfire early warning system was tested in a real controlled experiment, in which 44 sensors were deployed in strategically selected locations at varying distances from the fire. To enhance early detection, three Artificial Intelligence (AI) models were developed using AutoEncoders (AEs) and Long-Short-Term Memory (LSTM), and these were compared to a simple threshold-based (NO-AI) model. All AI models, especially the LSTM-based model, were able to extract more valuable information from the CO<sub>2</sub> records, activating up to 56% more sensors than the NO-AI model in less time and tracking potential fire front propagation based on wind patterns. Therefore, the system not only improves early fire detection models but also effectively supports firefighting operations.https://www.mdpi.com/1424-8220/25/7/2012CO<sub>2</sub> sensorsLSTMAutoEncodersearly warning systemwireless sensors networkwildfire detection |
| spellingShingle | Alessio De Rango Luca Furnari Fabio Cortale Alfonso Senatore Giuseppe Mendicino Wildfire Early Warning System Based on a Smart CO<sub>2</sub> Sensors Network Sensors CO<sub>2</sub> sensors LSTM AutoEncoders early warning system wireless sensors network wildfire detection |
| title | Wildfire Early Warning System Based on a Smart CO<sub>2</sub> Sensors Network |
| title_full | Wildfire Early Warning System Based on a Smart CO<sub>2</sub> Sensors Network |
| title_fullStr | Wildfire Early Warning System Based on a Smart CO<sub>2</sub> Sensors Network |
| title_full_unstemmed | Wildfire Early Warning System Based on a Smart CO<sub>2</sub> Sensors Network |
| title_short | Wildfire Early Warning System Based on a Smart CO<sub>2</sub> Sensors Network |
| title_sort | wildfire early warning system based on a smart co sub 2 sub sensors network |
| topic | CO<sub>2</sub> sensors LSTM AutoEncoders early warning system wireless sensors network wildfire detection |
| url | https://www.mdpi.com/1424-8220/25/7/2012 |
| work_keys_str_mv | AT alessioderango wildfireearlywarningsystembasedonasmartcosub2subsensorsnetwork AT lucafurnari wildfireearlywarningsystembasedonasmartcosub2subsensorsnetwork AT fabiocortale wildfireearlywarningsystembasedonasmartcosub2subsensorsnetwork AT alfonsosenatore wildfireearlywarningsystembasedonasmartcosub2subsensorsnetwork AT giuseppemendicino wildfireearlywarningsystembasedonasmartcosub2subsensorsnetwork |