Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning

Benzekri, Wiame; Moussati, Ali El; Moussaoui, Omar; Berrajaa, Mohammed

doi:10.14569/ijacsa.2020.0110564

Cited by 55 publications

(23 citation statements)

References 9 publications

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“…Each of References [21]- [24] uses the sensor network that is generally comprised of multiple fire detectors located separately. The sensor networks are also used for the outdoor fire detection with the Artificial Intelligence (AI)-based decision systems, which are specifically designed by using NNs in [25], [26], and Deep Learning model in [27]. Moreover, References [28]- [30] combine the sensor networks with vision-based systems in order to detect the outdoor fire accurately.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection

Nakıp

Güzeliş

Yıldız³

2021

IEEE Access

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We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR), Nonlinear Perceptron (NP), Multi-Layer Perceptron (MLP), Kendall-τ combined with MLP, Probabilistic Bayesian Neural Network (PBNN), Long-Short Term Memory (LSTM), and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire, where this duration is 22 s for the second-best model. Moreover, we present that the execution time of rTPNN is acceptable for real-time applications.

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Section: Related Workmentioning

confidence: 99%

Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection

Nakıp

Güzeliş

Yıldız³

2021

IEEE Access

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“…Recently, deep learning (DL) technique, a part of machine learning, is gradually applied in various fields. For example, Panahi (2020) used convolutional neural networks and recurrent neural networks to predict the probability of flash flood (Panahi et al, 2020); Kumar (2020) used deep learning model to complete the prediction of ground water depth (Kumar et al, 2020); Benzekri (2020) employed the deep learning model to construct an early forest fire detection system (Benzekri et al, 2020). In general, DL model performed a satisfactory ability of classification and regression.…”

Section: Instructionmentioning

confidence: 99%

Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

Zhang¹,

Wang

Chen³

et al. 2021

Preprint

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The losses and damage caused by landslides are countless in the world every year. However, the existing approaches of landslide susceptibility mapping cannot fully meet the requirement of landslide prevention, and further excavation and innovation are also needed. Therefore, the main aim of this study is to develop a novel deep learning model namely landslide net (LSNet) to assess the landslide susceptibility in Hanyin County, China, meanwhile, support vector machine model (SVM) and kernel logistic regression model (KLR) were employed as reference model. The inventory map was generated based on 259 landslides, the training dataset and validation dataset were respectively prepared using 70% landslides and the remaining 30% landslides. The variance inflation factor (VIF) was applied to optimize each landslide predisposing factor. Three benchmark indices were used to evaluate the result of susceptibility mapping and area under receiver operating characteristics curve (AUROC) was used to compare the models. Result demonstrated that although the processing speed of LSNet model is the slowest, it still significantly outperformed its corresponding benchmark models with validation dataset, and has the highest accuracy (0.950), precision (0.951), F1 (0.951) and AUROC (0.941), which reflected excellent predictive ability in some degree. The achievements obtained in this study can improve the rapid response capability of landslide prevention for Hanyin County.

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“…Considering the operating characteristics of the scraper conveyor, based on the existing advanced sensing technologies, relevant experts and scholars have overcome the problems in assessing the operating condition of the scraper conveyor, and a certain research base has been established, but it is still limited to the system platform design, wireless sensing technology development, and data processing algorithm optimization [6]. The current research mostly uses sensors and measurement devices to measure different parameters of the chain drive system, and the main measurement tools include tension sensors, angle sensors, electromagnetic sensors, Hall elements, and electromagnetic detection devices [7]. In turn, a wireless sensor network is designed for data transmission, and data analysis of different parameters is used to assess the operating status of the equipment for condition monitoring and fault determination of the scraper conveyor [8].…”

Section: Current Status Of Researchmentioning

confidence: 99%

Optimized Design of Mechanical Chain Drive Based on a Wireless Sensor Network Data Algorithm

Zhuang

Ding

et al. 2021

Journal of Sensors

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In this paper, we use a wireless sensor network data algorithm to optimize the design of mechanical chain drive by conducting an in-depth study of the mechanical chain drive optimization. We utilize the crowdsourcing feature of the swarm-wise sensing network for assisted wireless sensor networking to achieve crowdsourcing-assisted localization. We consider a framework for crowdsourcing-assisted GPS localization of wireless sensor networks and propose two recruitment participant optimization objectives, namely, minimum participants and time efficiency, respectively. A model and theoretical basis are provided for the subsequent trusted data-driven participant selection problem in swarm-wise sensing networks. The sprocket-chain engagement frequency has the greatest influence on the horizontal bending-vertical bending composite in different terrain conditions. The dynamic characteristics under working conditions are most influenced, while the scraping of the scraper and the central groove significantly influenced horizontal bending and vertical bending. Under load conditions, the amplitude of the scraper and central groove scraping increases significantly, which harm the dynamics of the scraper conveyor. By monitoring the speed difference between the head and tail sprockets and the overhang of the scraper, the tensioning status of the scraper conveyor chain can be effectively monitored to avoid chain jamming and chain breakage caused by the loose chain, thus improving the reliability and stability of the scraper conveyor.

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Early Forest Fire Detection System using Wireless Sensor Network and Deep Learning

Cited by 55 publications

References 9 publications

Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection

Recurrent Trend Predictive Neural Network for Multi-Sensor Fire Detection

Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

Optimized Design of Mechanical Chain Drive Based on a Wireless Sensor Network Data Algorithm

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