Forest Fire Prevention Research Articles

Research on Strengthening the Management of Forest and Grassland Fire Prevention Ability in Ethnic Areas of Southwest China

Purpose - Nowadays, forest and grassland fires are very serious in the world, and the area of forests and grasslands burned each year is close to one thousandth of the world’s existing forest and grassland area. The serious impact of fires on ecology not only affects the development of the world economy, but also seriously damages the human living environment.
 Design/Methodology/Approach - How to strengthen the construction of forest and grassland fire prevention capabilities has undoubtedly become an urgent problem that countries around the world need to solve.
 Findings - Wu Yingda et al. (2022) summarized their research and concluded that in 1988, China issued and implemented the “Forest Fire Prevention Regulations”. In 2010, the Ministry of Agriculture issued the “National Grassland Fire Emergency Plan”. In 2012, the General Office of the State Council issued the “National Forest Fire Emergency Plan”.
 Research Implications - Liu Yongbi (2001) believes that these factors have led to complex fire prevention work in the southwestern region, especially in ethnic minority areas, and difficulties in fire source management and prediction. Based on this, how to scientifically and effectively prevent and manage forest and grassland fires in southwestern ethnic minority areas, and find more practical and feasible forest and grassland fire prevention strategies is an important way for China to make breakthroughs in forest and grassland fire prevention work in southwestern ethnic areas, and even the entire southwestern region.

Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study

The current research aims to assess the precision of forest fire detection using CNN and VGG16 models, specifically in the context of fire identification. While both models have demonstrated significant promise in visual pattern recognition, a comprehensive analysis regarding their specific benefits in forest fire identification is still needed. The rationale behind this research stems from the significance of promptly identifying forest fires as a preemptive measure to mitigate their detrimental effects on the environment and society. The employed approach involves the application of transfer learning techniques on a diverse and extensive dataset encompassing different forest fire scenarios. The dataset was used to train both CNN and VGG16 models. The test results indicated that the CNN model achieved a forest fire detection accuracy of 96%, while VGG16 achieved 98% accuracy. The primary objective of this research is to enhance comprehension regarding the merits and demerits of each model in the context of forest fire identification scenarios. While VGG16 exhibits marginally superior performance in identifying forest fires, this discrepancy offers valuable insight into the practical applicability of these two models for fire detection in real-world scenarios. These findings establish a solid basis for the advancement of more dependable and efficient early detection technology in the prevention and management of forest fires in the future. This can be accomplished by capitalizing on the unique capabilities of each model to optimize their performance in practical scenarios.

Application of Artificial Neural Network in Wildfire Early Prediction Systems

The preservation of forest ecosystems is of vital importance to life on our planet. The increased losses of forests due to fires make the task of forest fire prevention of crucial significance. The present paper describes the development of an artificial neural network (ANN) for forest fire early prediction. The ANN predictor consists of two layers with 5 neurons in the hidden layer. It is trained through backpropagation of an error learning algorithm and is validated to provide prediction with a high degree of accuracy. An additional advantage of the designed predictor is the use of a limited number of input data based on weather and moisture conditions and of an output of a prior computed probability for fire. The training and validation datasets consist of 82 records of real measurement data. The developed and validated ANN can contribute to improvement of the current forest fire prediction systems.

Monchique’s Innovation Laboratory—A Space for Dialogue and Knowledge Sharing to Foster Community-Based Disaster Risk Reduction

BRIDGE (bridging science and local communities for wildfire risk reduction) is an action–research project developing a community-based disaster risk reduction (CBDRR) process which is being fostered through a collaborative innovation laboratory (InnoLab). BRIDGE integrates different forms of knowledge and action, linking science and local communities to reduce their vulnerability and to enhance strategies for forest fire risk reduction. Applied in Monchique, a forest-fire-prone municipality in Portugal, the InnoLab creates a space for dialogue and knowledge sharing between multiple actors that, directly or indirectly, manage forest territories. BRIDGE attempts to facilitate social learning about forest fire risks, strengthen collaborative networks and enhance adaptive capacities (socially and institutionally) for forest fire prevention. This paper shares results of the InnoLab activities, which include (i) school community activities on wildfire risk perception; (ii) participatory mapping of Monchique’s vulnerabilities and risks; (iii) participatory sessions to create the community visioning for Monchique’s territory, to define priorities and capacities to be improved, and to identify functions and responsibilities to act upon; and (iv) workshop on CBDRR strategies for action. Lessons are shared on how InnoLab can represent an innovative participatory approach to promote CBDRR processes in forest-fire-prone territories by both contributing to the recognition of local knowledge systems and encouraging the active role of forest communities through strengthened local networks for a more lasting commitment to forest fire risk reduction policy.

Utilizing Grid Data and Deep Learning for Forest Fire Occurrences and Decision Support: A Case Study in the Ningxia Hui Autonomous Region

In order to investigate the geographical distribution of forest fire occurrences in the Ningxia Hui Autonomous Region, this study employs advanced modeling techniques, utilizing diverse data sources, including fuel, Gross Domestic Product (GDP), population, meteorology, buildings, and grid data. This study integrates deep learning Convolutional Neural Networks (CNNs) to predict potential fire incidents. The research findings can be summarized as follows: (i) The employed model exhibits very good performance, achieving an accuracy of 84.35%, a recall of 86.21%, and an Area Under the Curve (AUC) of 87.67%. The application of this model significantly enhances the reliability of the forest fire occurrence model and provides a more precise assessment of its uncertainty. (ii) Spatial analysis shows that the risk of fire occurrence in most areas is low-medium, while high-risk areas are mainly concentrated in Longde County, Jingyuan County, Pengyang County, Xiji County, Yuanzhou District, Tongxin County, Xixia District, and Yinchuan City, which are mostly located in the southern, southeastern, and northwestern regions of Ningxia Hui Autonomous Region, with a total area of 2191.2 square kilometers. This underscores the urgent need to strengthen early warning systems and effective fire prevention and control strategies in these regions. The contributions of this research include the following: (i) The development of a highly accurate and practical provincial-level forest fire occurrence prediction framework based on grid data and deep learning CNN technology. (ii) The execution of a comprehensive forest fire prediction study in the Ningxia Hui Autonomous Region, China, incorporating multi-source data, providing valuable data references, and decision support for forest fire prevention and control. (iii) The initiation of a preliminary systematic investigation and zoning of forest fires in the Ningxia Hui Autonomous Region, along with tailored recommendations for prevention and control measures.

Forest fires in a changing climate: risk assessment and management in Leiria national forest, Portugal

Forest ecosystems are vital for sustainable development and human well-being globally and in Europe. Sustainably managed forests are fundamental in combating natural disasters and providing multiple important goods and services for humans and the environment. However, with increasing climate change and its associated effects, forests have become severely and regularly prone to fires. This is seriously threatening forest protection, human safety, the economy, and biodiversity. In this context, understanding future forest fire risks, susceptibility, hazards, and fire prevention is essentially needed. This study thus examines the forest fire risks and hazards in Leiria National Forest (Mata Nacional de Leiria), Portugal using the 2017 forest fire as a benchmark. With the adoption of GIS and remote sensing techniques and data, vegetation type (NDMI), human factors (roads and settlement proximity), and terrain characteristics (slope and aspect) were assessed to map fire risk. Through multi-criteria analysis, these data were integrated to generate a forest fire risk index. Results demonstrate that about 46% of the study area is within high-risk and risky zones, 50% is considered moderate-risk fire zones and 3% is classified as low and risk-free zones. Sensitivity analysis indicated that high-risk areas are mostly low moisture coniferous fuel types while risk-free areas are high moisture deciduous fuel types. Further, it was established that the observed high-risk and risky zones are attributed mostly to proximity to settlements and roads and little topographical influence. The study thus suggests an increased future forest fire risk under the prevailing conditions and a hiking potential of increased burnt areas. We thus proposed effective proactive measures and adaptive management approaches to prevent and mitigate the devastating impacts of forest fires within the study location.

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Sichuan Province preserves numerous rare and ancient species of plants and animals, making it an important bio-genetic repository in China and even the world. However, this region is also vulnerable to fire disturbance due to the rich forest resources, complex topography, and dry climate, and thus has become one of main regions in China needing wildfire prevention. Analyzing the main driving factors influencing wildfire incidence can provide data and policy guidance for wildfire management in Sichuan Province. Here we analyzed the spatial and temporal distribution characteristics of wildfires in Sichuan Province based on the wildfire spot data during 2010–2019. Based on 14 input variables, including climate, vegetation, human factors, and topography, we applied the Pearson correlation analysis and Random Forest methods to investigate the most important factors in driving wildfire occurrence. Then, the Logistic model was further applied to predict wildfire occurrences. The results showed that: (1) The southwestern Sichuan Province is a high-incidence area for wildfires, and most fires occurred from January to June. (2) The most important factor affecting wildfire occurrence is monthly average temperature, followed by elevation, monthly precipitation, population density, Normalized Difference Vegetation Index (NDVI), NDVI in the previous month, and Road kernel density. (3) The Logistic wildfire prediction model yielded good performance, with the area under curve (AUC) values higher than 0.94, overall accuracy (OA) higher than 86%, true positive rate (TPR) values higher than 0.82, and threat score (TS) values higher than 0.71. The final selected prediction model has an AUC of 0.944, an OA of 87.28%, a TPR of 0.829, and a TS of 0.723. (4) The results of the prediction indicate that extremely high danger of wildfires (probability of fire occurrence higher than 0.8) is concentrated in the southwest, which accounted for about 1% of the area of the study region, specifically in Panzhihua and Liangshan. These findings demonstrated the effectiveness of the Logistic model in predicting forest fires in Sichuan Province, providing valuable insights regarding forest fire management and prevention efforts in this region.

A novel aerodynamic layout design of composite wing unmanned aerial vehicle based on canard configuration

With the development of information technology and technology, more and more cutting-edge industries have come into view of the public, among which one of the most representative technologies is UAV. The emergence of UAVs has reduced the labor cost of some businesses, such as farming, forest fire prevention, maritime patrol and so on. In response to the common problems of long preparation time and poor maneuverability of unmanned aerial vehicles, this paper studies the aerodynamic layout of unmanned aerial vehicles and proposes a new layout. Firstly, the aerodynamic layout of typical UAVs is introduced and compared and analyzed. Then, a new type of compound wing UAV is designed by screening and analyzing the wing, flight analysis, main wing and UAV production process with Reynolds number. Subsequently, simulation experiments were conducted to analyze and optimize the aerodynamic layout performance of the designed UAVs, thereby verifying the reliability of the proposed method.. Finally, the entire content is summarized and analyzed.

A novel aerodynamic layout design of composite wing unmanned aerial vehicle based on canard configuration

With the development of information technology and technology, more and more cutting-edge industries have come into view of the public, among which one of the most representative technologies is UAV. The emergence of UAVs has reduced the labor cost of some businesses, such as farming, forest fire prevention, maritime patrol and so on. In response to the common problems of long preparation time and poor maneuverability of unmanned aerial vehicles, this paper studies the aerodynamic layout of unmanned aerial vehicles and proposes a new layout. Firstly, the aerodynamic layout of typical UAVs is introduced and compared and analyzed. Then, a new type of compound wing UAV is designed by screening and analyzing the wing, flight analysis, main wing and UAV production process with Reynolds number. Subsequently, simulation experiments were conducted to analyze and optimize the aerodynamic layout performance of the designed UAVs, thereby verifying the reliability of the proposed method.. Finally, the entire content is summarized and analyzed.

An Improved YOLOv5s-Seg Detection and Segmentation Model for the Accurate Identification of Forest Fires Based on UAV Infrared Image

With the influence of climate change and human activities, the frequency and scale of forest fires have been increasing continuously, posing a significant threat to the environment and human safety. Therefore, rapid and accurate forest fire detection has become essential for effective control of forest fires. This study proposes a Forest Fire Detection and Segmentation Model (FFDSM) based on unmanned aerial vehicle (UAV) infrared images to address the problems of forest fire occlusion and the poor adaptability of traditional forest fire detection methods. The FFDSM integrates the YOLO (You Only Look Once) v5s-seg, Efficient Channel Attention (ECA), and Spatial Pyramid Pooling Fast Cross-Stage Partial Channel (SPPFCSPC) to improve the detection accuracy of forest fires of different sizes. The FFDSM enhances the detection and extraction capabilities of forest fire features, enabling the accurate segmentation of forest fires of different sizes and shapes. Furthermore, we conducted ablation and controlled experiments on different attention mechanisms, spatial pyramid pooling (SPP) modules, and fire sizes to verify the effectiveness of the added modules and the adaptability of the FFDSM model. The results of the ablation experiment show that, compared to the original YOLOv5s-seg model, the models fused with the ECA and SPPFCSPC achieve an improved accuracy, with FFDSM showing the greatest improvement. FFDSM achieves a 2.1% increase in precision, a 2.7% increase in recall, a 2.3% increase in mAP@0.5, and a 4.2% increase in mAP@0.5:0.95. The results of the controlled experiments on different attention mechanisms and SPP modules demonstrate that the ECA+SPPFCSPC model (FFDSM) performs the best, with a precision, recall, mAP@0.5, and mAP@0.5:0.95 reaching 0.959, 0.870, 0.907, and 0.711, respectively. The results of the controlled experiment on different fire sizes show that FFDSM outperforms YOLOv5s-seg for all three fire sizes, and it performs the best for small fires, with a precision, recall, mAP@0.5, and mAP@0.5:0.95 reaching 0.989, 0.938, 0.964, and 0.769, respectively, indicating its good adaptability for early forest fire detection. The results indicate that the forest fire detection model based on UAV infrared images (FFDSM) proposed in this study exhibits a high detection accuracy. It is proficient in identifying obscured fires in optical images and demonstrates good adaptability in various fire scenarios. The model effectively enables real-time detection and provides early warning of forest fires, providing valuable support for forest fire prevention and scientific decision making.

Multipurpose deep learning-powered UAV for forest fire prevention and emergency response

This paper presents a customized UAV designed for rescue and safety purposes in the forest sector. The UAV features a durable F450 frame quadcopter with four 1000KV brushless motors and a KK2.1 Flight Control Board for stability and manoeuvrability with a runtime of 90 min. It incorporates a Raspberry Pi camera for real-time video streaming, enabling efficient identification of individuals in need of assistance. The GSM module allows contactless communication, ensuring streamlined and safe interaction. A motor controls the lid of the customizable first aid kit box, facilitating efficient aid delivery. The Neo-6 M GPS module provides accurate localization of the drone and individuals in distress with a horizontal position accuracy of 2.5 m. The UAV collects temperature and humidity data using the DHT 11 sensor having +/- 2 degreesC and +- 5% accuracy respectively. This sensor employs advanced deep learning models, including artificial neural networks (ANN) and generative adversarial networks (GANs), for real-time forest fire prediction with an accuracy of 90.7 % The integration of GANs enhances accuracy through synthetic data generation. Moreover, all these components are interfaced using a Raspberry Pi4 and a GUI, providing a smooth user control experience and end-to-end information for quick and effective emergencyresponse.

The Synergy of Local Government and Academia in Addressing Peatland Fires in Siak Regency with a Technology-Based Approach

This study aims to explore the synergy between the local government and academia in mitigating peatland fires in Siak Regency. In the fields of social humanities, science, and technology, this study uses an exploratory qualitative method. The study reveals the important role of the local government in disaster mitigation through regional regulations, policies, and synergy between related departments. Through the regional regulation on forest fire prevention and handling, the local government has involved the Regional Disaster Management Agency (BPBD) and other supporting units in mitigation efforts. In addition, academics have also contributed by developing a technology-based peatland fire detection system. The main results of this study show that collaboration between the local government and academia is very significant in peatland fire mitigation efforts. The role of the local government through Governor's Decree No. 9 of 2020 and Regent's Decree No. 4 of 2022, as well as synergy between departments, encourages more effective steps in preventing and handling peatland fires. In addition, the contribution of academics to producing a technology-based peatland fire mitigation technology design opens up the potential for innovative and sustainable solutions. These findings have significant implications, as they highlight the importance of collaboration between the local government and academia in facing the challenges of peatland fire mitigation in Siak Regency. This synergy allows a better understanding of the problem, the development of more effective policies, and the application of advanced technology in preventing and handling peatland fires.

Energy Optimization Efficiency in Wireless Sensor Networks for Forest Fire Detection:

Wireless Sensor Networks (WSNs) have the potential to play a significant role in forest fire detection and prevention. However, limited resources, such as short battery life pose challenges for the energy efficiency and longevity of WSN-based IoT networks. This paper focused on the energy efficiency aspect and proposed the ECP-LEACH protocol to optimize energy consumption in forest fire detection cases. The proposed protocol consists of two main components: a threshold monitoring module and a sleep scheduling module. The threshold monitoring module continuously monitors energy consumption and triggers sleep mode for nodes surpassing the predetermined threshold. The ECP-LEACH protocol offers a promising solution for improving energy efficiency in WSN-based IoT networks for forest fire detection. By optimizing sleep scheduling and duty cycles, the ECP-LEACH protocol enables significant energy savings and extended network lifetime

Deep Learning Based Fire Detection and Fire Extinguisher Using Sound Wave

Abstract: Forest fires pose a significant threat to both human life and the environment, resulting in devastating losses of forests, properties, and homes. These recurring disasters are a global concern, prompting extensive research and the development of various solutions to address the issue. Detecting and combating forest fires have been particularly challenging, especially when dealing with vast forested areas. In this study, we propose a novel approach that leverages modern technologies, specifically Artificial Intelligence (AI) and computer vision methods. By utilizing convolutional neural networks, our platform aims to accurately recognize and detect smoke and fire from still images or video input obtained through cameras. The success of the method relies on the choice of the algorithm and the quality of the datasets used, which will be split into training and testing sets for evaluation. Through this innovative approach, we hope to contribute to the effective prevention and early detection of forest fires, safeguarding ecologically healthy forests and the environment.

CNN Based Forest Fire Monitoring System

Abstract: As a type of natural resource, forests are vital for our existence. Wildfires are a significant threat to humans and other living beings, with the development of satellite technology it can be continuously monitored and controlled. Accurate real-time monitoring of wildfires is not only an important part of forest fire prevention, but also an important means of effectively controlling the spread of wildfires and reducing economic losses. Therefore, we need a highly mobile early forest fire warning system to ensure the safety of forest resources. In view of this, we propose a forest fire monitoring method that uses convolutional neural network to detect fire from images which can monitor the presence of fire in the forest arena.

Forest Fire Driving Factors and Fire Risk Zoning Based on an Optimal Parameter Logistic Regression Model: A Case Study of the Liangshan Yi Autonomous Prefecture, China

Planning the analyses of the spatial distribution and driving factors of forest fires and regionalizing fire risks is an important part of forest fire management. Based on the Landsat-8 active fire dataset of the Liangshan Yi Autonomous Prefecture from 2014 to 2021, this paper proposes an optimal parameter logistic regression (OPLR) model, conducts forest fire risk zoning research under the optimal spatial analysis scale and model parameters, and establishes a forest fire risk prediction model. The results showed that the spatial unit of the optimal spatial analysis scale in the study area was 5 km and that the prediction accuracy of the OPLR was about 81%. The climate was the main driving factor of forest fires, while temperature had the greatest influence on the probability of forest fires. According to the forest fire prediction model, mapping the fire risk zoning, in which the medium- and high-risk area was 6021.13 km2, accounted for 9.99% of the study area. The results contribute to a better understanding of forest fire management based on the local environmental characteristics of the Liangshan Yi Autonomous Prefecture and provide a reference for related forest fire prevention and control management.

BACSSOC: A novel clustering method for mobile forest protection using wireless sensor network with lower energy consumption and lower latency

The utilization of Mobile Forest Protection Using Wireless Sensor Network (Mobile-FPWSN) has exhibited notable advantages in mitigating economic losses in forestry and enhancing the efficiency of fire prevention, thereby leading to its widespread adoption in the forestry protection industry. However, in the current developmental stage of Mobile-FPWSN, the impact of sensor nodes on the system is often disregarded in forest fire prevention and control. Latest studies have indicated that clustering techniques exhibit noteworthy benefits, including a decrease in energy consumption and an extension of system life. However, current clustering methodologies do not comprehensively address the concerns of latency, energy consumption, and delay while operating under Mobile-FPWSN conditions. In order to tackle these challenges, a novel clustering method called Boltzmann adaptive chaotic salp swarm optimization clustering (BACSSOC) has been proposed for Mobile-FPWSN. This method aims to significantly prolong the system’s lifetime, efficiently reduce energy consumption, and minimize system latency to the greatest extent possible. BACSSOC dramatically reduces the energy consumption of cluster heads during the adaptive clustering phase of Mobile-FPWSN, thereby decelerating the depletion of node energy and improving the forest fire monitoring time in comparison with other state-of-the-art algorithms. To evaluate the efficacy of BACSSOC in comparison to ML-AEFA, PCFMO, and SOA-EACR algorithms in mobile FPWSNs, we have conducted a unique forest fire simulation experiment based on a realistic forest fire scenario. The results have unequivocally demonstrated that BACSSOC surpasses ML-AEFA, PCFMO, and SOA-EACR algorithms, exhibiting advantages of at least 15% in reducing node energy consumption, 7% in decreasing system latency, and 9% in increasing system lifespan. These findings highlight the remarkable potential of BACSSOC as an effective network clustering solution in mobile FPWSNs.

Effect of phosphorus/nitrogen/carbon component ratio in aqueous flame retardant on the fire prevention of wood substrate

AbstractHerein, various of aqueous halogen‐free flame retardants (FRs) were fabricated by controlling the weight ratio of phosphorus/nitrogen/carbon compounds. The prepared FRs showed good dispersion level at ambient conditions and easily adhering feature on the wood substrate. Owing to the synergistic flame‐retardant effect of phosphorus/nitrogen/carbon compounds on wood, the FR1 coated wood (W‐FR1) could form non‐pores, compact, and continuous phospho‐carbonaceous layer under fire. Meanwhile, W‐FR1 exhibited a higher LOI (59.5%) and UL‐94 rating (V‐0). Moreover, the ignition time of W‐FR1 was significantly delayed from 35 to 570 s. Additionally, W‐FR1 showed 31.6% and 47.1% reductions in THR and TSP, respectively, 99.3% increment in char residue, as compared to the untreated wood. The present research offers a promising aqueous flame retardant for potential forest fire prevention.

Optimizing fire extinguishing effects of pneumatic sandblasting fire extinguishers based on experiments and CFD-DEM coupling simulations

This study focuses on addressing specific issues and research gaps related to pneumatic sandblasting effects for forest fire prevention and control. The primary objective is to determine the sensitivity of geometric parameters and recommend optimal design parameters for the extinguisher. The investigation employs a combination of Computational Fluid Dynamics-Discrete Element Methods (CFD-DEM) coupling simulations and experiments. The simulation and experimental results suggest flat outlets have the farthest sandblasting distance. Further experimentation shows that the outlet with different diameters show different sandblasting distances and accumulation effects, and the increased air duct length leads to decreased average airflow velocity at the outlet. In addition, the blade number of impellers was also studied for a longer sandblasting distance. Finally, the study found that the optimal parameters (79 cm air duct with the flat outlet of 8 cm diameter, 20-blade impeller), resulted in longer fire extinguishing length in fire extinguishing experiments compared to the original extinguisher. The findings provide valuable insights for improving the effectiveness of fire extinguishers and enhancing forest fire management strategies. The research outcomes have the potential to make a substantial impact in mitigating the devastating effects of forest fires and safeguarding natural resources and human lives.

Advanced Solar-Powered Fire Detection System: A Wireless Sensor Node Approach to Early Warning and Forest Fire Prevention

This article presents the design and implementation of a solar fire detection system using a Wireless Sensor Node (WSN). The system incorporates a temperature sensor, Bluetooth module, and intelligent battery charging management to provide real-time monitoring and early detection of forest fires, while also ensuring energy conservation and battery protection. By employing a DC-DC converter, the system maintains a stable 9V supply to the Arduino board under various power supply conditions. The integration of the temperature sensor and Bluetooth module enables accurate temperature data transmission to mobile or computer terminals for real-time monitoring. The energy-saving and battery protection features optimize charging cycles and prolong battery life, contributing to environmental sustainability and enhanced safety for communities at risk. The successful testing of the system demonstrates its reliability and potential as an effective early warning system to combat the devastating effects of forest fires.