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  • Open Access


    Fusion-Based Deep Learning Model for Automated Forest Fire Detection

    Mesfer Al Duhayyim1, Majdy M. Eltahir2, Ola Abdelgney Omer Ali3, Amani Abdulrahman Albraikan4, Fahd N. Al-Wesabi2, Anwer Mustafa Hilal5,*, Manar Ahmed Hamza5, Mohammed Rizwanullah5

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1355-1371, 2023, DOI:10.32604/cmc.2023.024198

    Abstract Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development, regulatory measures, and their implementation elevating the environment. Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe. Therefore, the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent. Therefore, this paper focuses on the design of automated forest fire detection using a fusion-based deep learning (AFFD-FDL) model for environmental monitoring. The AFFD-FDL technique involves the design… More >

  • Open Access


    Unmanned Aerial Vehicle Assisted Forest Fire Detection Using Deep Convolutional Neural Network

    A. K. Z Rasel Rahman1, S. M. Nabil Sakif1, Niloy Sikder1, Mehedi Masud2, Hanan Aljuaid3, Anupam Kumar Bairagi1,*

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3259-3277, 2023, DOI:10.32604/iasc.2023.030142

    Abstract Disasters may occur at any time and place without little to no presage in advance. With the development of surveillance and forecasting systems, it is now possible to forebode the most life-threatening and formidable disasters. However, forest fires are among the ones that are still hard to anticipate beforehand, and the technologies to detect and plot their possible courses are still in development. Unmanned Aerial Vehicle (UAV) image-based fire detection systems can be a viable solution to this problem. However, these automatic systems use advanced deep learning and image processing algorithms at their core and can be tuned to provide… More >

  • Open Access


    Intelligent Deep Learning Enabled Wild Forest Fire Detection System

    Ahmed S. Almasoud*

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1485-1498, 2023, DOI:10.32604/csse.2023.025190

    Abstract The latest advancements in computer vision and deep learning (DL) techniques pave the way to design novel tools for the detection and monitoring of forest fires. In this view, this paper presents an intelligent wild forest fire detection and alarming system using deep learning (IWFFDA-DL) model. The proposed IWFFDA-DL technique aims to identify forest fires at earlier stages through integrated sensors. The proposed IWFFDA-DL system includes an Integrated sensor system (ISS) combining an array of sensors that acts as the major input source that helps to forecast the fire. Then, the attention based convolution neural network with bidirectional long short… More >

  • Open Access


    An Energy-Efficient Wireless Power Transmission-Based Forest Fire Detection System

    Arwa A. Mashat, Niayesh Gharaei*, Aliaa M. Alabdali

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 441-459, 2022, DOI:10.32604/cmc.2022.024131

    Abstract Compared with the traditional techniques of forest fires detection, wireless sensor network (WSN) is a very promising green technology in detecting efficiently the wildfires. However, the power constraint of sensor nodes is one of the main design limitations of WSNs, which leads to limited operation time of nodes and late fire detection. In the past years, wireless power transfer (WPT) technology has been known as a proper solution to prolong the operation time of sensor nodes. In WPT-based mechanisms, wireless mobile chargers (WMC) are utilized to recharge the batteries of sensor nodes wirelessly. Likewise, the energy of WMC is provided… More >

  • Open Access


    Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

    Ahmad A. A. Alkhatib*, Qusai Abed-Al

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1071-1087, 2022, DOI:10.32604/iasc.2022.020461

    Abstract Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data’s accuracy… More >

  • Open Access


    Using Image Processing Technology and General Fluid Mechanics Principles to Model Smoke Diffusion in Forest Fires

    Liying Zhu*, Ang Wang, Fang Jin

    FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.6, pp. 1213-1222, 2021, DOI:10.32604/fdmp.2021.017572

    Abstract In the present study, the laws of smoke diffusion during forest fires are determined using the general principles of fluid mechanics and dedicated data obtained experimentally using an “ad hoc” imaging technology. Experimental images mimicking smoke in a real scenario are used to extract some “statistics”. These in turn are used to obtain the “divergence” of the flow (this fluid-dynamic parameter describing the amount of air that converges to a certain place from the surroundings or vice versa). The results show that the divergence of the smoke depends on the outside airflow and finally tends to zero as time passes.… More >

  • Open Access


    A Perceptron Algorithm for Forest Fire Prediction Based on Wireless Sensor Networks

    Haoran Zhu1, Demin Gao1,2,*, Shuo Zhang1

    Journal on Internet of Things, Vol.1, No.1, pp. 25-31, 2019, DOI:10.32604/jiot.2019.05897

    Abstract Forest fire prediction constitutes a significant component of forest management. Timely and accurate forest fire prediction will greatly reduce property and natural losses. A quick method to estimate forest fire hazard levels through known climatic conditions could make an effective improvement in forest fire prediction. This paper presents a description and analysis of a forest fire prediction methods based on machine learning, which adopts WSN (Wireless Sensor Networks) technology and perceptron algorithms to provide a reliable and rapid detection of potential forest fire. Weather data are gathered by sensors, and then forwarded to the server, where a fire hazard index… More >

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