Early Forest Fire Detection and Prevention using Ultra-Low-Power Co-Processors with Convolutional Neural Networks

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.

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.

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.

A multi-sign method of laser control of the fire situation in the forest is proposed. In the process of burning in the forest, a significant amount of greenhouse gases and heat is released into the atmosphere. The main gases released are carbon dioxide, methane, nitrogen oxide, and carbon monoxide. An overview of publications on methods of early detection of forest fires is presented. An approach to improvement the sensitivity of fire detection in a forest by means of simultaneously registering all emitted gases is considered. The approach makes use of the differential absorption method for each gas with an integrated laser node emitting at several specially selected wavelengths. The optimization of the proposed method is carried out, as a result of which recommendations are developed for the construction of a highly sensitive system for early detection of forest fires at specified energy consumption.

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.

The progress in the development of high-performance advanced materials and cost-effective manufacturing technologies is paving the way for large-scale, low-cost fabrication of miniaturized sensors for meteorological and environmental monitoring applications. This paper reviews sensors based on microelectromechanical systems (MEMS) and nanotechnology with great potential for use in wireless sensor networks (WSN) aimed at the prevention and early-stage detection of wildfires.

Regional cooperation in South East Europe (SEE) is of great importance today, yet various initiatives to stimulate it have not yet led to any significant results. This has undermined peace and stability in SEE. This paper considers various initiatives aimed at stimulating cooperation in SEE, including EU policies towards the region. It then discusses some aspects of regional cooperation, primarily economic. A multinational case study the Advanced Systems for Prevention and Early Detection of Forest Fires (ASPires) project is discussed in detail as a successful example of SEE cooperation on cross-border issues that have civilian, ecological, and defense implications.

This study employs a sociometric analysis of Community Concern and Participation in Implementing the Lancang Kuning Nusantara Application for Forest and Land Fire Prevention in Toba Regency, Indonesia. With a sample of 401 respondents, the study aims to comprehensively understand social interactions and community engagement in this context. Results indicate that the perception score (X(i.1)) and participation score (X(i.2)) are high or significant, reflecting positive community awareness and active involvement in fire prevention activities. However, the acceptability score (X(i.3)) is low or insignificant, suggesting potential dissatisfaction or skepticism regarding the effectiveness or implementation of current programs. Furthermore, standard deviation analysis reveals that variability in respondent ratings is generally within control, with only 2 out of 15 testing points showing high standard deviation, while 13 are within acceptable limits. Nevertheless, the proportion of respondents who rate low (PRMR) exceeds the desired threshold of 10% across all variables, indicating that many respondents have low evaluations. These findings suggest a need for improved communication, education, and potential adjustments to enhance the acceptability and effectiveness of the fire prevention initiatives and the Lancang Kuning Nusantara System application.

Early Forest Fire Detection and Verification using Optical Smoke, Gas and Microwave Sensors

Brain tumors are formed due to the abnormal growth of the cells that get multiplied and become in uncontrollable perspective. Tumors can damage brain cells by pressing down the skull, which consociate begins in, which negatively affects human health. In advanced stages, a brain tumor is a more dangerous infection that cannot be relieved. Brain tumor detection and early prevention are necessary in today's world. An extreme learning machine (ELM) is a widely adopted algorithm in machine learning. It is proposed to use classification models in brain tumor imaging. This classification is based on the techniques implemented: Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN). CNN efficiently solves the convex optimization problem and is faster, requiring less human effort. The algorithmic architecture of GAN uses two neural networks, pocking one against the other. These networks are implemented in various fields to classify brain Tumor images. The present study mainly aims to introduce a new proposed classification system of preschool children brain imaging with Hybrid Convolutional Neural Networks and with the techniques of GAN. The proposed technique is compared with the existing hybrid-CNN and hybrid-GAN techniques. The outcomes are encouraging because the loss is deduced, and the accuracy facet increases. The proposed system achieved a training accuracy of 97.8% and a validation accuracy of 89%. The outcomes of the studies show that ELM in the platform of GAN for preschool children brain imaging classification has achieved higher predictive performance than the traditional classification mechanisms in increasingly complex scenarios. Time elapsed for training brain images samples finds inference value for training samples and time elapsed value increased by 28.9855%. Approximation ratio for cost based on probability, finding Approximation ratio for low probability range is increased by 88.1%. The combination of CNN, GAN and hybrid-CNN, hybrid-GAN, and hybrid CNN + GAN, compared with the proposed hybrid system, increased Detection latency for low range learning rate by 3.31%.

Forest wealth is the biggest proportionality in the environmental sustainability. The forest wealth is mainly endangered by various natural calamities such as flood, hurricane, fire,volcano, land striding and so on. The forest is also threatened with man made damages such as afforestation, wild-trafficking, animal trade, illegal expansions. The human oriented calamities can be controlled with the existence of severe laws and regulations. The natural calamities are uncontrollable in nature. The only phenomena which can be controlled by human is forest fire. This proposed work provides insights on the detection of the forest fire in early stages through satellite imaging and the same is supplied to the nearest monitoring stations. In these stations we apply deep image neural network models such as InceptionV3, VGG-16, Resnet 50 and DenseNet 201 to determine the presence of forest fire or not through binary classification. The results show that the Densenet 201 has the deepest convolutional layer among all these methods, provides the accuracy of 98.46 % by outperforming all other models.

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 fires cause disastrous damage to both human life and ecosystem. Therefore, it is essential to detect forest fires in the early stage to reduce the damage. Convolutional Neural Networks (CNNs) are widely used for forest fire detection. This paper proposes a new backbone network for a CNN-based forest fire detection model. The proposed backbone network can detect the plumes of smoke well by decomposing the conventional convolution into depth-wise and coordinate ones to better extract information from objects that spread along the vertical dimension. Experimental results show that the proposed backbone network outperforms other popular ones by achieving a detection accuracy of up to 52.6 AP.1

Forest fire detection system using barrier coverage in wireless sensor networks