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# A Data Fusion of IoT Sensor Networks for Decision Support in Forest Fire Suppression

João P. Oliveira, Miguel Lourenço, Luís Oliveira, André Mora, Henrique Oliveira

► To cite this version:

João P. Oliveira, Miguel Lourenço, Luís Oliveira, André Mora, Henrique Oliveira. A Data Fusion of IoT Sensor Networks for Decision Support in Forest Fire Suppression. 4th IFIPIoT, Nov 2021, Virtual, Netherlands. pp.96-108, 10.1007/978-3-030-96466-5_7. hal-04471533

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Submitted on 21 Feb 2024

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# A Data Fusion of IoT Sensor Networks for Decision Support in Forest Fire Suppression

João P. Oliveira¹,²✉, Miguel Lourenço¹,², Luís Oliveira¹,², André Mora¹,² and Henrique Oliveira³,⁴

¹ Dep. of Electrical Engineering, NOVA School of Science and Technology, Caparica, Portugal

² Centre for Technologies and Systems (CTS) – UNINOVA, Caparica, Portugal
jpao@fct.unl.pt, mag.lourenco@campus.fct.unl.pt, l.oliveira@fct.unl.pt, atm@uninova.pt

³ Instituto de Telecomunicações, Lisboa, Portugal

⁴ Instituto Politécnico de Beja, Beja, Portugal
hjmo@lx.it.pt

**Abstract.** The extent and impact of wildfires are expected to increase as a consequence of climate changes, pushing forest and firefighting management decision-making process to be supported by Cyber-Physical-Systems (CPS) that are capable to promote collaborative decision processes. New IoT tools and frameworks fuse scientific knowledge and diversity of sensor data will contribute to improve strategic resources allocation, with the aim of protecting lives, assets, and the environment. The presented IoT CPS which is tuned as a decision support system (DSS) is adapted for the Portuguese wildfire context. It is composed by a geographic information system (GIS) online framework, a mobile client application and a set of portable multi-sensor devices.

**Keywords:** Multi-Sensor Technology, Internet of Things, Cyber-Physical-System, Decision Support System, wildfires.

## 1 Introduction

The concept of interconnection of physical objects based on the Internet of Things (IoT) is forcing significant changes in traditional sensor monitoring microsystems, namely in terms of cost, flexibility, security, resilient communications. Moreover, all processes involve the heterogeneous integration of diverse technologies including physical, communications and networking, and cloud computing. This cooperation between virtual cyber and real physical object entities represents the evolution of the IoT into a Cyber-Physical System (CPS) strategy, [1].

The dynamics of the physical processes integrated with those of the software and networking, facilitates the design and analysis of complex smart systems. For example, the optimization of modern systems applications, including decision support systems for firefighting as illustrated in Figure 1. In this example, several multi-parameter sensors and actuators IoT nodes are positioned at several entry points inside this ecosystem.
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A decision support system with fusion of data originated from distinct sources, including sensors that interface with the physical domain, constitutes a CPS in which the software and hardware are closely interlaced. In these types of complex scenarios, usually, several persons are involved in the decision-making process, requiring an appropriate computer-based collaborative support system.

<figure><img src="image_3.png" /><figcaption>Fig. 1. A Cyber-Physical System concept for forest fire fighting.</figcaption></figure>

In the last 20 years, Portugal has been severely affected by large wildfires, with almost three million hectares burnt, resulting in dramatic consequences in both social, economic, and environmental domains. The strong incidence of forest fires in the country is due to a joint effect of several reasons namely the significant changes in the climate during the year, ranging from rainy to extreme drought periods and hot temperatures (especially during the summer season), leading to a greater production of plant biomass, thus culminating in large wildfires [2]. To support the management of fire-fighting resources during these wildfire events, a strong effort in developing new approaches based on geographic information systems (GIS) has been conducted by the scientific community [3,4].

The classical fire-fighting system centralizes much of the information at the command centers. In the field, commanders, with limited computing resources, mostly resort to military cartography for registering and planning of the operational response. Additionally, the access and registration of historical data from the wildfire scenario is still a bottleneck due to limited reliable resources being one of them the communication subsystem.

In past years, several methodologies were implemented, aiming the development of better DSSs for wildfire management based on GIS technology [5,6]. In Greece, VirtualFire was proposed in 2013 [7] and later, the same research team proposed AEGIS [8] as a WebGIS Tool, to aid in prevention and combat of wildfires. It integrates spatial and map data, as well as vehicle's location, water, and fuel supplies, among other infrastructures, weather data, fire risk assessment maps and fire spread modelling. The
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Wideland Fire Decision Support System (WFDSS) [9,10] is the system used in the US, offering some similar features as the VirtualFire, and also includes a module for financial risk analysis. In Portugal, the MacFIRE Project is another WebGIS, launched by the Municipality of Mação, incorporating mapping data, vehicle's location, fire front severity classification and its location (manually done), ignition points of a fire, among other features [11].

The use of Wireless Sensor Networks (WSN) has been extensively studied in the literature mainly as fire detection systems to alert of potential wildfire spots in an early stage of propagation [12,13]. These solutions can be extended to monitor the progression of wildfires and provide very useful data for more accurate fire spread simulations. However, they present downsides that reduce its applicability since when it comes to wildfire detection and monitoring, typically WSNs need to be designed for large areas involving a significant number of devices spread over the terrain. This poses multiple challenges such as when nodes fail or are destroyed by the fire, they need to be repaired or replaced, causing significant logistical challenges, considering such an extensive area. Also, technical challenges like the need for a network interface in each node or in a central gateway, which may be challenging since forests may not have a consistent internet or mobile coverage. Energy harvesting on the nodes is also a concern especially if they are installed in the tree trunks as they may not have much sun and wind exposure.

Hence, passive WSNs do not offer a significant value especially considering other existing fire detection solutions that cover extensive areas and do not require as much maintenance [14,15].

This paper presents a Decision Support System (DSS) whose effectiveness relies on a reliable fusion of data coming from several and distinct sources. To support this integration, the DSS consists of an online GIS framework, a mobile client application, and a multi-sensor IoT node to support this integration. Section 2 presents a high-level architecture for the proposed DSS, whose major components blocks are described in sections 3 and 4.

## 2 A Data Fusion based DSS high-level architecture

A Decision Support System (DSS) that leverages multi-sensor technologies and GIS functionalities is proposed to be a tool for decision-makers to analyze spatial and temporal data to take effective decisions during wildfires. Figure 2 shows the general architecture of the presented DSS.
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<figure><img src="image_4.png" /><figcaption>Fig. 2. The proposed DSS architecture.</figcaption></figure>

This DSS is divided into three components: (1) a GIS service; (2) a mobile client application; and (3) a portable IoT Multi-Sensor Device:

1. The GIS online service was configured with a set of feature layers that host the relevant operational data which were published as an GIS feature service, allowing its use by several client applications through its Application Programming Interface (API). The available layers are used to store multiple types of data, such as: (i) the obstacles in the road that might affect the route taken by vehicles; (ii) the vehicles position and state, which is represented by different symbology to inform whether a certain vehicle is in a dangerous situation; (iii) the water supply locations; and (iv) fire-front severity and their location. The data is stored on a GIS database for later analysis and fire reproduction, allowing the decision makers to study the effect of their strategies. These layers can be easily and centrally configured and according to the user needs.

2. The mobile client application is the gateway to access all the operational data on the GIS online service, providing the user with GIS functionality to analyze and manage this type of data. This application contains visualization dashboards to facilitate the decision-making process, as well as alerts that warns the user about certain events. Finally, a road network dataset is configured in a local file geodatabase resident in the client application device's storage. The application generates routes between multiple places and find the shortest path to the nearest water supply location. All the work done on the client application is stored on a local sync-enabled file geodatabase that can be periodically synchronized with the GIS online webservice.
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3. The IoT Multi-Sensor Device provides critical sensor data and gives insight about the current circumstances of the theatre of operations. Its main objective is to geolocate firefighting vehicles, whilst monitoring parameters such as temperature, humidity, oxygen and carbon monoxide concentrations inside the vehicle and the temperature surrounding it. It also collects remote sensed infrared data to create thermal images from scanning the region affected by a wildfire during the aftermath analysis. The client application generates alerts based on this sensor data that can be a lifesaving in situations, such as, a forest track machine operator working in a low oxygen environment.

The data fusion solution presented aims at aggregating multiple sources of heterogeneous data and display it in a comprehensive way to be an important support tool for decision-makers during wildfire scenarios.

Data fusion allows the merging of multiple and heterogeneous sources of data to produce a more consistent, organized, and accurate information by reducing redundancy and uncertainty on the data [16]. By coupling the derived information from the multiple data sources alongside the empirical knowledge of wildfire management, decision-makers can use this system as a tool to enhance their situational awareness and take faster, comprehensive, and more informed decisions.

The core component of this solution is the mobile application where users can interact with the system and visualize the relevant operational data. This application gathers multiple sources of data being the IoT Multi-Sensor Device one of the key sources of data.

The IoT Multi-Sensor node is integrated in the system in such a way that leverages both its multi-sensor technologies, with the available mobile device sensors and network interface to form a unified sensing node. By using the existing smartphone platform, it can act as a complementary portable gateway to relay the IoT Multi-Sensor node's data to the server.

## 3 Mobile Application

The Mobile Client Application is the core component of the proposed DSS that aggregates all the relevant data and functionality in a practical User Interface (UI). An initial assessment was made to gather the necessary requirements based on the end-user's feedback to which the system's architecture was designed around.

Figure 3 shows the key features implemented in the Mobile Client Application as well as the information sources that are jointly processed and correlated.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** The Mobile Client Application key features.</figcaption></figure>

One of the most important features is the ability to operate offline, since internet connectivity may not always be available on the field, thus potentially undermining an effective and fast operational response. To achieve this goal, a workflow that includes a data synchronization step is designed to enable a seamless user experience in both online and offline environments.

As mentioned, a synchronization mobile geodatabase is created from a sync-enabled GIS online service that is stored locally on the mobile device, allowing the user to work offline by editing the geodatabase locally and later synchronizing its changes with the service. While offline, the mobile geodatabase keeps track of local edits that are later synchronized with the service, and changes made on the service by other users can also be received when the device is back online. The main advantages of this type of workflow are: not be dependent on a continuous internet connectivity, and not having to pre-plan map areas ahead of time to take them offline onto the mobile device, thus improving the system's flexibility.

By leveraging the GIS technology, we could also implement key functionalities that enable the user to create, manage and store spatial and non-spatial data. To achieve this, the ArcGIS Runtime SDK for Android is used [17], which provides a set of tools and documentation to implement the necessary features in a customized application. An GIS webservice is created through a data schema that was designed for the user requirements, to provide all the necessary operational data that is stored in the user account. This enables the establishment of a collaborative environment, where multiple users can be working on the same data, which is critical during wildfires,

A custom road Network Dataset was also created for the terrain knowledge (in this case from the Municipality of Mação in Portugal) that allows users to generate routes between multiple locations in this region, thus enabling to find the shortest path to the closest water supply locations. By incorporating a dynamic component, this feature al-
lows generating routes according to the conditions of the road network. This is partic-
ularly important during wildfires since certain roads cannot be crossed due to the fire
front being nearby or existing fallen trees or holes in the road. In this scenario, routes
are calculated taking into consideration these dynamic factors that will avoid putting
firefighters in dangerous situations and speed-up the operational response.

Finally, an intuitive and easy-to-use user interface (UI) is the key for allowing users
to quickly navigate through the system and assess the situation on the field. This re-
quires a comprehensive data visualization experience for the user, to provide location
awareness and accelerate the planning of effective strategies to suppress wildfires. To
meet this requirement, the UI is organized into three areas:

* Alerts area, allows the user to get access to alerts about vehicles in danger or abnor- mal sensor readings from the IoT Multi-Sensor Device that could indicate the pres- ence of dangerous situations.

* Map area, displays a map and provides GIS functionalities that allow the user to manipulate geographical data associated with the map such as adding, editing or deleting features, adding or removing attachments, generating routes between locations.

* Data Visualization area, allows the visualization of data from features on the map such as sensor readings from the IoT Multi-Sensor Device and information about the vehicles on the field. It also provides relevant statistics such as the number of vehicles.

To address the limitation of the mobile platform screen dimensions, three tabs were
implemented to display each of the three areas mentioned above on smartphone's UI.
Figure 4 shows the Mobile Client Application UI with visualization tabs.

<figure><img src="image_6.png" /><figcaption>Fig. 4. The User Interface of the application.</figcaption></figure>

To provide the network dataset to the client application there were two options to be
considered: the first option was to provide it through a web service that would require
an internet connection upon its use; the second option was to be available through a
geodatabase on the device. Considering that the internet connectivity may not always
be available, the network dataset was provided through a geodatabase on the local device's storage.

The application lets the user find the closest water supply spot on the map relative to his location. These water supply spots are represented in a particular Feature Layer and classified by either fixed water supply, which includes lakes, dams or reservoirs, or mobile water supply such as pump trucks. To generate a route, it is important to consider the existence of obstacles such as fallen trees. A Feature Layer was created to contain them and is used to determine the closest water supply point.

In Figure 5 it is evident the impact of existing obstacles on the road that will yield a different path generated. By introducing this dynamic component, users can have access to an updated road network according to real-time conditions that should prevent them from wasting time that is a critical resource during a wildfire and may be the difference between having a wildfire under control or not. Moreover, this feature could potentially avoid firefighters from getting into life-threatening scenarios such as what happened in Pedrógraça Grande, Portugal in 2017 [18]. Further work will be conducted to parameterize the Network Dataset according to the type of vehicle, considering a vehicle's dimension and weight when calculating paths to avoid going through impassable roads for certain vehicles.

<figure><img src="image_7.png" /><figcaption>Fig. 5. Demonstration of the closest water supply functionality: with and without obstacles.</figcaption></figure>

## 4 The CPS-IoT Multi-Sensor Node

The role of this node is to provide local field sensor data that can help decision-makers not only assess the circumstances in the field and the state of the firefighting assets but also to outline strategies to mitigate the effects of wildfires. It provides important data to simulate fire propagation according to the weather conditions and detect reignition spots by scanning the region affected by a wildfire during the aftermath analysis.

The presented IoT multi-sensor node has three main operating modes which can provide critical data for generating alerts to ensure both the vehicle and its crew's safety
(Alarm mode), acquire thermal data to find potential re-ignition spots in the aftermath
of a wildfire (Thermal Mapping mode) and provide local weather data that will help
obtain more accurate fire spread simulations (Weather Station mode). Figure 6 shows
the architecture of the IoT Multi-Sensor Device with its components.

<figure><img src="image_8.png" /><figcaption>Fig. 6. The IoT Multi-Sensor Device Architecture.</figcaption></figure>

In the Alarm mode, the IoT Multi-Sensor Device is used to monitor the vehicle's
surrounding environment and its crew by sensing the CO and O₂ gas concentrations
inside and outside the vehicle, the O₂ gas concentrations on the vehicle's engine admis-
sion, and the temperatures surrounding the vehicle. A group of single-pixel thermal
infrared (IR) sensors is used to monitor the temperatures surrounding the vehicle. Hav-
ing these multiple sensors equally spaced makes it possible to get a 360° coverage of
the vehicle's surroundings. The acquired sensor data is sent to the mobile application
where corresponding alerts are generated. A low O₂ gas concentration on the vehicle's
engine admission may affect its operability potentially putting the vehicle and even its
crew in a dangerous situation. On the other hand, a low O₂ and high CO gas concentra-
tions inside the vehicle's cabin can cause the crew to pass out or even put their lives at
risk, therefore it is essential to leverage this system to ensure both humans and equip-
ment safety in an ever dynamic and dangerous scenario such as in a wildfire.

In the Thermal Mapping mode, the IoT Multi-Sensor node is used to acquire thermal
data from areas affected by wildfires in the aftermath of these events, to find possible
reignition spots. To achieve this, the IoT Multi-Sensor Device is equipped with a 768-
pixel thermal IR sensor capable of detecting a 24x32 matrix of spot temperatures to
build a thermal image. This data is sent to the mobile application where it is used to generate a colored map that facilitates the identification of hot spots. Upon their detection, firefighters can quickly intervene to avoid a potential reignition.

In the Weather Station mode, the IoT Multi-Sensor node is used to measure weather parameters such as the air temperature and humidity, the concentration of O₂ gas, and the wind direction and speed. This data is sent to the mobile application and can be used to perform more accurate fire spread simulations. By having a local weather station on the wildfire locations, fire spread simulators can use more reliable data that may consider the local microclimate created by extreme wildfires. This will theoretically lead to more accurate predictions of the wildfire's expected trajectory and, simultaneously, determine the risk areas for its spread.

The IoT Multi-Sensor node is built around the ESP32 microcontroller (MCU) [19] to which all the sensors are connected and providing sensor data to connected clients. The ESP32 MCU is configured to work as a web server in the softAP mode. In this mode, it acts as an Access Point (AP) that supports up to 10 clients and provides a JSON-encoded message containing real-time sensor measurements from the sensors onboard. The mobile client application connects to this AP and performs a GET request periodically to its web server that returns the updated JSON message with the sensor's measurements. All the data post-processing is conducted on the mobile application. This device is prepared to be reconfigurable allowing the easy integration of more sensors.

As referred before, the CPS-IoT Multi-Sensor node hosts a temperature and humidity sensor, a GNSS receiver to acquire the device's location, a CO and O₂ sensors to measure gas concentrations, a multi-pixel and 5 single-pixel IR sensors to measure and map the surrounding temperature. The validation of the IoT Multi-Sensor node focused on assessing the reliability of the multi-pixel IR thermal sensor.

This sensor produces 768 temperature values organized into a 24x32 matrix that go through an initial pre-processing phase where a non-uniform mean filter is applied to the data that gives greater weight to pixels closer to the center and less weight to the border pixels that are more susceptible to noise, resulting in a more homogeneous image. Furthermore, in this phase the undetermined values are removed by using an algorithm that detects their presence in pixels and replaces those pixel values with the mean value of the surrounding pixels. After the pre-processing phase, the sensor was put to test in a prepared scenario using a controlled fire. Several tests were conducted to know the impact of speed and distance to the heat source in the measurement quality. Initially, measurements were taken to determine the impact of distance in the sensor granularity. As expected, the greater the distance the lesser prominent was the heat source in the resulting image. The second phase of test focused on assessing the impact of both distance and speed on the detection of heat sources.

The results of these tests present on Figure 7 show that the greater the speed and distance the more significant the impact on the heat source identification accuracy, nevertheless, it demonstrates that the chosen multi-pixel IR thermal sensor performs well under these test conditions. The last component of its validation was to test the connection with the mobile client application that worked seamlessly thus validating this system component.
<figure><img src="image_9.png" /><figcaption>Fig. 7. Experiment conducted at a distance of 20 meters from the heat source (a) with no movement, and (b) moving at 30 km/h.</figcaption></figure>

## 5 Conclusions

A system for effective wildfire firefighting demands the evolution of the simplistic IoT sensor network approach into a Cyber-Physical System with smart data fusion. In addition, given the difficulty of guaranteeing continuous network communication in the field, it is necessary to provide enough processing, sensing and local communication to the remote fire brigade. To respond to the latter requirement, the CPS must reinforce the computing capability at the edge.

In this paper an example Cyber-Physical System is presented which consists on a DSS adapted to the Portuguese wildfire firefighting. Several modules are integrated to support the firefighting operational management and decision making. The system has been validated in a controlled environment, showing the effectiveness of the proposed solution for supporting decision making in a fire suppression context.

The integration of multi-sensor technologies alongside the mobile client application sensors and functionalities has proven to provide major value in leveraging the mobile device's network capabilities and redundancy with the IoT Multi-Sensor node flexibility in adding new sensors that not only allow the user to have access to information in the field but also this data can be used to improve local fire spread simulations. This implementation does not require to have a WSN put in place enabling the flexibility of having a single node already installed in the firefighter's vehicle to which they can connect locally to its network with their mobile device running the mobile client application and get its data.
# 6 Acknowledgements

This work was financially supported by FCT (National Foundation of Science and Technology) within the Research Unit CTS – Centre of Technology and Systems, UIDB/00066/2020, and the Project foRESTER (PCIF/SSI/0102/2017 - 400k€ total budget, http://www.forester.pt).

# References

1. A. A. Nazarenko and L. M. Camarinha-Matos, "Towards collaborative Cyber-Physical Systems," 2017 International Young Engineers Forum (YEF-ECE), 2017, pp. 12-17, doi: 10.1109/YEF-ECE.2017.7935633

2. Ferreira-Leite, F., Ganho, N., Bento-Gonçalves, A., Botelho, F., 2017. Iberian atmospheric dynamics and large forest fires in mainland Portugal. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2017.08.033

3. Pultar, E., Raubal, M., Cova, T.J., Goodchild, M.F., 2009. Dynamic GIS Case Studies: Wild-fire Evacuation and Volunteered Geographic Information. Transactions in GIS. https://doi.org/10.1111/j.1467-9671.2009.01157.x

4. Smith, A.K., Dragićević, S., 2019. A four-dimensional agent-based model: A case study of forest-fire smoke propagation. Transactions in GIS. https://doi.org/10.1111/tgis.12551

5. Sakellariou, S., Tampekis, S., Samara, F., Sfougaris, A., Christopoulou, O., 2017. Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires. Journal of Forestry Research. https://doi.org/10.1007/s11676-017-0452-1

6. Abedi Gheshlaghi, H., Feizizadeh, B., Blaschke, T., Lakes, T., Tajbar, S. Forest fire susceptibility modeling using hybrid approaches. Transactions in GIS. 2020; 00: 1–23. https://doi.org/10.1111/tgis.12688

7. Kalabokidis, K., Athanasis, N., Gagliardi, F., Karayiannis, F., Palaiologou, P., Parastatidis, S., & Vasilakos, C. (2013). Virtual Fire: A web-based GIS platform for forest fire control. Ecological Informatics, 16, 62–69. https://doi.org/10.1016/j.ecoinf.2013.04.007

8. Kalabokidis K., Ager A., Finney M., Athanasis N., Palaiologou P., Vasilakos C. 2016. AEGIS: A Wildfire Prevention and Management Information System. Natural Hazards and Earth System Sciences 16(3):643-661. Available at: https://www.fs.usda.gov/treesearch/pubs/52996

9. Peter Noble, Travis B Paveglio, Exploring Adoption of the Wildland Fire Decision Support System: End User Perspectives, Journal of Forestry, Volume 118, Issue 2, March 2020, Pages 154–171, https://doi.org/10.1093/jofore/fvz070

10. Noonan-Wright, E. K., Opperman, T. S., Finney, M. A., Zimmerman, G. T., Seli, R. C., Elenz, L. M., ... Fiedler, J. R. (2011). Developing the US wildland fire decision support system. Journal of Combustion, 2011. https://doi.org/10.1155/2011/168473

11. J. Curva, M. Lourenço, N. Paulino, J. P. Oliveira, L. Oliveira and H. Oliveira, "Infrared Fire Alarm for Vehicle Protection," 2020 International Young Engineers Forum (YEF-ECE), Costa da Caparica, Portugal, 2020, pp. 19-24, doi: 10.1109/YEF-ECE49388.2020.9171813.

12. Abdullah, S., Bertalan, S., Masar, S., Coskun, A., & Kale, I. (2017). A wireless sensor network for early forest fire detection and monitoring as a decision factor in the context of a complex integrated emergency response system. Em 2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS). IEEE.
13. Zhu, Y., Xie, L., & Yuan, T. (2012). Monitoring system for forest fire based on wireless sensor network. Em Proceedings of the 10th World Congress on Intelligent Control and Automation. IEEE.

14. Gandia A., Criado A. and Rallo M. (1994). "El Sistema BOSQUE, Alta Tecnologia en Defensa del Medio Ambiente". DYNA, pp.34-38, n. 6.

15. Laurenti A., and Neri A. (1996), "Remote Sensing, Communications and Information Technologies for Vegetation Fire Emergencies", Proceedings of TIEMEC'96, Montreal.

16. Silva Girão, P., Postolache, O., & Pereira, J. (1970). Data Fusion, Decision-Making, and Risk Analysis: Mathematical Tools and Techniques. In Modeling and Simulation in Science, Engineering and Technology (pp. 1–50). https://doi.org/10.1007/978-0-8176-4804-6_7.

17. ArcGIS Developers (2021) ArcGIS Runtime API for Android. https://developers.arcgis.com/android/. Accessed 18 Jun 2021.

18. Comissão Técnica Independente (2017) Análise e apuramento dos factos relativos aos incêndios que ocorreram em Pedrógão Grande, Castanheira de Pera, Ansião, Alvaiázere, Figueiró dos Vinhos, Arganil, Góis, Penela, Pampilhosa da Serra, Oleiros e Sertã, entre 17 e 24 de junho de 2017. https://www.parlamento.pt/Documents/2017/Outubro/Relat%C3%B3rio-CTI_VF%20.pdf. Accessed 18 Jun 2021.

19. Espressif Systems (2021) ESP32-WROOM-32 Datasheet, Ver. 3.1. https://www.espressif.com/sites/default/files/documentation/esp32-wroom-32 datasheet_en.pdf. Accessed 18 Jun 2021.