Early Detection of Forest Fire using Deep Image Neural Networks

Many animals and plants are on the endangered list because of the forest fire caused by natural calamities and also human activities. The frequent forest fires have contributed significantly to air pollution and global warming. This paper proposes a Wireless Sensor Network (WSN) framework for continuous monitoring and real-time detection of forest fires by using temperature, humidity, gaseous sensors integrated into MICAz motes and the IR camera sensor for surveillance purpose. The proposed WSN architecture intends to notify forest fire faster than conventional approaches and also helps in anticipating the directional flow of the fire.

Forest fires pose significant threats to both natural ecosystems and human communities due to their unpredictable nature and capacity for widespread destruction. Identifying and mitigating fires in the trunk, ground, and canopy of forests is crucial for reducing their adverse effects on the ecosystem and climate. The detrimental impacts of forest fires, such as the exacerbation of the greenhouse effect, the hastening of global warming, and the modification of climatic patterns, underscore the urgent necessity for the creation of efficient detection systems. This study presents a real-time Universal Trust Model (UTM) that is specifically designed for the early forest fires detection (FFD) using an intelligent wireless sensor network and machine learning approaches. Our method seeks to reduce fire detection time and improve the reliability of the detection process. This is achieved by employing environmental indicators and moisture levels to swiftly identify fires. The intelligent WSN functions by partitioning the forest into suitable clusters and intelligently positioning sensor nodes to guarantee extensive coverage and effective data transmission to the sink. The proposed UTM system’s core component is the computation of trust ratings for every sensor node. These ratings consider communication, energy, and data trust factors to evaluate the reliability of the data being delivered. This integrated trust model enhances the robustness and accuracy of fire detection, especially under difficult environmental conditions. Furthermore, a machine learning regression model is deployed at the base station to augment the precision of fire detection. This is accomplished by examining essential attributes such as temperature, humidity, and CO2 concentrations. We have conducted thorough experiments using actual datasets consisting of 7200 samples to confirm the efficacy of our proposed UTM in detecting forest fires at an early stage. The results suggest that our system obtains a high rate of data processing and a reduced time delay in comparison to existing systems. This renders it a promising solution for the imperative need to promptly detect and prevent forest fires. Our technique combines trust mechanisms with machine learning algorithms to create a very advanced forest fire detection system.

Context: Forests are home to 80% of terrestrial life; however, they have been seriously threatened as millions of its hectares are lost each year due to forest fires. Aim: To elicit the requirements, methods, techniques, technological elements, situations, and specifications to structure a current, compact, generic, and low-cost technological model that allows early alert or detection of forest fires and provide timely support in decision-making. Method: We carried out a Preliminary Literature Review (PLR) on Scopus digital database strictly following the guidelines for systematic literature reviews proposed by B. Kitchenham with some improvements. Results: The PLR process obtained 15 primary studies as of July 2020, which highlight emerging technologies to address the forest fires’ issues. Conclusions: Forest fires represent an unprecedented danger to life, so there is a broad range of possibilities for their early detection since time ago. Emerging technologies give the science new alternatives to develop compact, economic, accessible, and functional proposals for early warning about this phenomenon.KeywordsForest firesEmerging technologiesEarly alertPreliminary literature review

Forest fires are a persistent global problem, causing devastating consequences such as loss of human lives, harm to the environment, and substantial economic losses. To mitigate these impacts, the accurate prediction and early detection of forest fires is critical. In response to this challenge and living in the digital era of Artificial Intelligence (AI) and smart economies, there has been a growing interest in utilising AI mechanisms for forest fire management. This study provides an in-depth examination of the use of AI algorithms in the fight against forest fires. In particular, our paper starts with an overview of the forest fire problem, followed by a comprehensive review of various systems and approaches. This review includes a thorough analysis of the various works that have evaluated the factors that influence fire occurrence and severity, as well as those that focus on fire prediction and detection systems. The paper also explores the use of AI in adapting and restoring after the occurrence of forest fires. The paper concludes with an evaluation of the potential impact of AI on forest fire management and suggestions for future research directions, taking full advantage of novel technologies, such as 5G communications, Software Defined Networking (SDN), digital twins, federated learning and blockchain. Finally, the paper draws lessons and insights on the potential and limitations of AI in forest fire management, highlighting the need for further research and development in this field to maximise its impact and benefits.

Early detection of a forest fire can save our flora and fauna. Ad Hoc Wireless Sensor Networks (WSN) plays an important role in detecting forest fire. This article proposes a model for early detection of forest fire through predictive analytics. In this approach, the forest area is divided into different zones. Status of a zone, i.e., High Active (HA), Medium Active (MA), and Low Active (LA), is predicted by applying the semi-supervised classification technique. Each zone has static sensors, mobile sensors, and an Initiator node. Initiator nodes of LA and MA zone transfer their mobile nodes (MN) to the nearer HA zone for the quick prediction of forest fire by using the Random trajectory generation (RTG) technique. This technique generates the intermediate points between LA/MA to HA zone to create the movement path of MN. Compressed sensing based Gradient descent (GD) localization technique is used to track the movement of MN by the anchor nodes. This technique reduces the energy consumption of MN that causes an increase in network lifetime. The analysis of the localization error of MN during its traveling towards the HA zone increases the accuracy of its path detection. Thus the increase of sensor nodes in the HA zone results in transferring a huge amount of data from HA zone to base station for quick prediction of a forest fire.

This paper presents a drone-based early forest fire detection system. Using multiple on-board aerial sensors, thermal images, RGB images, and distance between forest fire points and drones can be captured and determined from the air. To take advantage of data from different sources for forest fire detection and confirmation, both deep learning-based and traditional computer vision algorithms are developed and employed. The on-board computer and ground station computer are designed to work collaboratively according to the different complexity and computational demands of sub-modules in this system. By integrating different sensor data with a two-phase strategy for potential early forest fire detection and confirmation, the proposed system achieves a relatively low false alarm rate and has good robustness in the outdoor real-time early forest fire detection experiments with an on-board computer installed on a DJI M300 drone.

Over the last few decades, forest fires are increased due to deforestation and global warming. Many trees and animals in the forest are affected by forest fires. Technology can be efficiently utilized to solve this problem. Forest fire detection is inevitable for forest fire management. The purpose of this work is to propose deep learning techniques to predict forest fires, which would be cost-effective. The mixed learning technique is composed of YOLOv4 tiny and LiDAR techniques. Unmanned aerial vehicles (UAVs) are promising options to patrol the forest by making them fly over the region. The proposed model deployed on an onboard UAV has achieved 1.24 seconds of classification time with an accuracy of 91% and an F1 score of 0.91. The onboard CPU is able to make a 3D model of the forest fire region and can transmit the data in real time to the ground station. The proposed model is trained on both dense and rainforests in detecting and predicting the chances of fire. The proposed model outperforms the traditional methods such as Bayesian classifiers, random forest, and support vector machines.

Forest fire is a common natural disaster, which has a great threat to ecological and human life safety. Compared with the traditional monitoring method, Satellite remote sensing can provide more timely and large-area monitoring in the process of early detection, dynamic tracking, and disaster assessment of forest fires. This paper proposed a forest fire detection method based on MODIS active fire products, by adding Normalized vegetation index (NDVI), slope, elevation, and other factors as identification conditions. To verify the reliability of the proposed method, this paper used the MODIS active fire products data in China in July 2018, the identification accuracy was up to 88.94%, and the missing detection rates were only 4.25%, the error was mainly due to the low spatial resolution of MODIS data. The validation results showed that the overall accuracy of the proposed method was very satisfactory, which can meet the real-time early warning of a forest fire. Moreover, taking a forest fire that occurred in Qinyuan County, Shanxi Province as an example, the paper analyzed how to use the identified forest fire points to monitor the fire and make fire-fighting plans, which proved that using the fire points data can provide scientific bases for forest fire monitoring and extinguishing.

Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.

Forest fires remain a major problem for the country’s forestry sector, requiring an analysis and rethinking of the fire safety system in the Russian Federation’s forests. This article examines the early detection and suppression of forest fires. It identifies a number of measures necessary to improve the effectiveness of forest fire protection, and provides recommendations for emergency response to forest fires and the use of federal reserve funds, the formation of which is provided for in Article 53.9 of the Forest Code of the Russian Federation.

Forest fires can be the cause of serious environmental and economic damages. For this reason a considerable effort has been directed toward the forest protection and fire fighting. In the early forest fire detection, Lidar technique present considerable advantages compared to the passive detection methods based on infrared cameras currently in common use, due its higher sensitivity and ability to accurately locate the fire. The combustion phase of the vegetable matter causes a great amount of water vapour emission, thus the water molecule behaviour will be studied to obtain a fire detection system ready and efficient also before the flame propagation. A first evaluation of increment of the water vapour concentration compared to standard one will be estimated by a numerical simulation. These results will be compared with the experimental measurements carried out into a cell with a CO₂ Dial system, burning different kinds of vegetable fuel. Our results and their comparison will be reported in this paper.

Forest fire detection system using barrier coverage in wireless sensor networks

Wireless sensor networks are widely used in environmental applications, like forest fire detection. Although forest fires occur relatively rarely, their number is increasing in Europe in the last years, so their manifestation must be early detected in order to prevent higher damages. To minimize needless communication between the sensor nodes for this usage, new data suppression technique using sliding window skylines is described in this paper. We experimentally evaluate our algorithm for continuous sliding windows skylines computation, and show its usability in practice.

Wildfires cause severe consequences, including property loss, threats to human life, damage to natural resources, biodiversity, and economic impacts. Consequently, numerous wildland fire detection systems were developed over the years to identify fires at an early stage and prevent their damage to both the environment and human lives. Recently, deep learning methods were employed for recognizing wildfires, showing interesting results. However, numerous challenges are still present, including background complexity and small wildfire and smoke areas. To address these challenging limitations, two deep learning models, namely CT-Fire and DC-Fire, were adopted to recognize wildfires using both visible and infrared aerial images. Infrared images detect temperature gradients, showing areas of high heat and indicating active flames. RGB images provide the visual context to identify smoke and forest fires. Using both visible and infrared images provides a diversified data for learning deep learning models. The diverse characteristics of wildfires and smoke enable these models to learn a complete visual representation of wildland fires and smoke scenarios. Testing results showed that CT-Fire and DC-Fire achieved higher performance compared to baseline wildfire recognition methods using a large dataset, which includes RGB and infrared aerial images. CT-Fire and DC-Fire also showed the reliability of deep learning models in identifying and recognizing patterns and features related to wildland smoke and fires and surpassing challenges, including background complexity, which can include vegetation, weather conditions, and diverse terrain, detecting small wildfire areas, and wildland fires and smoke variety in terms of size, intensity, and shape. CT-Fire and DC-Fire also reached faster processing speeds, enabling their use for early detection of smoke and forest fires in both night and day conditions.

The early detection and monitoring of forest fire is important for fire suppression quickly and reducing the loss of human and property. At present, forest fire monitoring techniques based on wireless sensor networks have drawn more and more attention from both academic and forestry field. In order to reduce the cost of monitoring system, it is necessary to monitor a biggest area with sensor nodes of given number. In this paper, based on the forest fire circular propagation model, the case where the sensors are located in a regular triangle grid is analyzed. Some results which relate the sensor node density to the forest fire monitoring quality are presented. The theoretical analysis and the results of Monte Carlo simulations show that, the expect value of the time to fire detection is proportional to the ration of node density to the fire propagation speed, and the expect value of burned area only depends on the node density, but it doesn’t depend on the distribution of the fire ignition and fire propagation model.