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House combustion is one of the main concerns for builders, designers, and property residents. Singular sensors were used for a long time in the event of detection of a fire, but these sensors can not measure the amount of fire to alert the emergency response units. To address this problem, this study aims to implement a smart fire detection system that would not only detect the fire using integrated sensors but also alert property owners, emergency services, and local police stations to protect lives and valuable assets simultaneously. The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the system. To get real-life data without putting human lives in danger, an IoT technology has been implemented to provide the fire department with the necessary data. Finally, the main feature of the proposed system is to minimize false alarms, which, in turn, makes this system more reliable. The experimental results showed the superiority of our model in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable.

Forest fires are one of the major environmental concerns, each year millions of hectares are destroyed over the world, causing economic and ecological damage as well as human lives. Thus, predicting such an environmental issue becomes a critical concern to mitigate this threat. Several technologies and new methods have been proposed to predict and detect forest fires. The trend is toward the integration of artificial intelligence to automate the prediction and detection of fire occurrence. This paper presents a comprehensive survey of the machine learning algorithms based forest fires prediction and detection systems. First, a brief introduction to the forest fire concern is given. Then, various methods and systems in forest fires prediction and detection systems are reviewed. Besides works that reported fire prediction and detection systems, studies that assessed the factors influencing the fire occurrence and risk are discussed. The main issues and outcomes within each study are presented and discussed.

Fire protection measures are taken to prevent fires or to keep the resulting damage as low as possible. The statistical effectiveness of fire protection measures can be derived from a large number of fires that have already occurred. With the research paper presented here, such proof of effectiveness is rendered for certain specific fire protection measures, such as installed fire detection and fire alarm systems, fire extinguishing systems, smoke and heat exhaust systems, as well as according to the type of fire service. The investigation is based on a systematically collected database of 5,016 building fire interventions with 1,216 real fires by 29 fire services across Germany. The results can be used by applying engineering methods for quantitative risk analyses, within the scope of the risk-based performance level oriented planning of object-specific protection strategies. In this way, the performance level can be achieved effectively, flexibly and economically.

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers.

Fires in high-rise residential buildings can lead to human casualties and significant property damage. Therefore, ensuring fire safety in these buildings is an urgent task. The results of the Fire Protection System simulation of the development of a fire in a residential apartment showed that open windows and doors of the room, as well as the wind speed outside the window, affect the dynamics of fire development and the spread of non-hazardous fire factors. However, when the wind speed outside the window is 7.0 m/s, due to draft and cooling, the hazardous factors of the fire do not reach critical indicators for humans. This makes the evacuation process safer for people. The impact of the position of windows and doors, and the wind outside the window on the time of detection or failure of fire detectors was determined. It was determined that even if the current regulatory requirements for installing fire detectors are met, when the wind speed outside the window is 7.0 m/s, only the smoke detector located in close proximity to the source of the fire and having increased sensitivity is triggered. The results demonstrate the need for an adaptive approach to the placement of detectors in high-rise residential buildings.

Fire detection and monitoring systems based on machine vision have been gradually developed in recent years. Traditional centralized deep learning model training methods transfer large amounts of video image data to the cloud, making image data privacy and confidentiality difficult. In order to protect the data privacy in the fire detection system with heterogeneous data and to enhance its efficiency, this paper proposes an improved federated learning algorithm incorporating computer vision: FedVIS, which uses a federated dropout and gradient selection algorithm to reduce communication overhead, and uses a transformer to replace a traditional neural network to improve the robustness of federated learning in the context of heterogeneous data. FedVIS can reduce the communication overhead in addition to reducing the catastrophic forgetting of previous devices, improving convergence, and producing superior global models. In this paper’s experimental results, FedVIS outperforms the common federated learning methods FedSGD, FedAVG, FedAWS, and CMFL, and improves the detection effect by reducing communication costs. As the amount of clients increases, the accuracy of other algorithmic models decreases by 2–5%, and the number of communication rounds required increases significantly; meanwhile, our method maintains a superior detection performance while requiring roughly the same number of communication rounds.

Tunnel fires pose a severe threat to human safety and infrastructure, necessitating the development of advanced and efficient fire detection systems. This paper presents a novel lightweight deep learning (DL) model specifically designed for real-time fire classification in tunnel environments. This model integrates MobileNetV3 for spatial feature extraction, Temporal Convolutional Networks (TCNs) for temporal sequence analysis, and advanced attention mechanisms, including Convolutional Block Attention Modules (CBAMs) and Squeeze-and-Excitation (SE) blocks, to prioritize critical features such as flames and smoke patterns while suppressing irrelevant noise. The model is trained on a custom dataset containing real tunnel fire incidents generated using a newly prepared dataset. This approach enhances the model generalization capabilities, enabling it to handle diverse fire scenarios, including those with low visibility, high smoke density, and variable ventilation conditions. Deployment optimizations, such as quantization and layer fusion, ensure computational efficiency, achieving an average inference time of 12ms/frame, making it suitable for resource-constrained environments like IoT and edge devices. The experimental results demonstrate that the proposed model achieves an accuracy of 96.5%, a precision of 95.7%, and a recall of 97.2%, significantly outperforming state-of-the-art (SOTA) models such as ResNet50 and YOLOv5 in both accuracy and real-time performance. Robustness tests under challenging conditions validate model reliability and adaptability, marking it as a critical advancement in tunnel fire detection systems. This study provides valuable insights into the design and deployment of efficient fire classification systems for safety-critical applications. The proposed model offers a scalable, high-performance solution for tunnel fire monitoring and establishes a benchmark for future research in real-time video-based classification under complex environmental conditions.

With the development of artificial intelligence, image recognition has seen wider adoption. Here, a novel paradigm image recognition system is proposed for detection of fires owing to the compression of lithium-ion batteries at recycling facilities. The proposed system uses deep learning method. The SparkEye system is proposed, focusing on the early detection of fires as sparks, and is combined with a sprinkler system, to minimize fire-related losses at affected facilities. Approximately 30,000 images (resolution, 800 × 600 pixels) were used for training the system to >90% detection accuracy. To fulfil the demand for dust control at recycling facilities, air and frame camera protection methods were incorporated into the system. Based on the test data and realistic workplace feedback, the best placements of the SparkEye fire detectors were crushers, conveyors, and garbage pits.

Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption.

In the recent past, a few fire warning and alarm systems have been presented based on a combination of a smoke sensor and an alarm device to design a life-safety system. However, such fire alarm systems are sometimes error-prone and can react to non-actual indicators of fire presence classified as false warnings. There is a need for high-quality and intelligent fire alarm systems that use multiple sensor values (such as a signal from a flame detector, humidity, heat, and smoke sensors, etc.) to detect true incidents of fire. An Adaptive neuro-fuzzy Inference System (ANFIS) is used in this paper to calculate the maximum likelihood of the true presence of fire and generate fire alert. The novel idea proposed in this paper is to use ANFIS for the identification of a true fire incident by using change rate of smoke, the change rate of temperature, and humidity in the presence of fire. The model consists of sensors to collect vital data from sensor nodes where Fuzzy logic converts the raw data in a linguistic variable which is trained in ANFIS to get the probability of fire occurrence. The proposed idea also generates alerts with a message sent directly to the user’s smartphone. Our system uses small size, cost-effective sensors and ensures that this solution is reproducible. MATLAB-based simulation is used for the experiments and the results show a satisfactory output.