Event Triggering Estimation for Cell-DEVS: Wildfire Spread Simulation Case

Dahmani, Youcef; Hamri, Maâmar El-Amine

doi:10.1109/ems.2011.16

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…We have identified five possible states that a cell can take (Figure 10). Each cell represents a limited area of the forest [23]:…”

Section: Problem Identificationmentioning

confidence: 99%

Specification of the State's Lifetime in the Devs Formalism by Fuzzy Controller

Dahmani¹,

Maamar²

2013

IJAIT

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This paper aims to develop a new approach to assess the duration of state in the DEVS formalism by fuzzy controller. The idea is to define a set of fuzzy rules obtained from observers or expert knowledge and to specify a fuzzy model which computes this duration, this latter is fed into the simulator to specify the new value in the model. In conventional model, each state is defined by a mean lifetime value whereas our method, calculates for each state the new lifetime according to inputs values. A wildfire case study is presented at the end of the paper. It is a challenging task due to its complex behavior, dynamical weather condition, and various variables involved. A global specification of the fuzzy controller and the forest fire model are presented in the DEVS formalism and comparison between conventional and fuzzy method is illustrated.

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“…We have identified five possible states that a cell can take (Figure 10). Each cell represents a limited area of the forest [23]:…”

Section: Problem Identificationmentioning

confidence: 99%

Specification of the State's Lifetime in the Devs Formalism by Fuzzy Controller

Dahmani¹,

Maamar²

2013

IJAIT

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“…Bayesian networks (BNs) were applied in previous studies on tsunamis [36] and rock fall hazards [35]. Researchers [37] used a BNN to predict and assess wildfire occurrences and burn severity and modeled the wildfire spread [38], wildfire ecological consequences [39], and risk of human fatality from fires in buildings [40]. In 2017, an approach that utilized BNs for wildfire economic losses was presented [41].…”

Section: Introductionmentioning

confidence: 99%

Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data

et al. 2022

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An effective forest-fire response is critical for minimizing the losses caused by forest fires. The purpose of this study is to construct a model for early fire detection and damage area estimation for response systems based on deep learning. First, a large-scale fire dataset with approximately 400,000 images is used to train and test object-detection models. The optimal backbone for the faster region-based convolutional neural network (Faster R-CNN) model is determined using a DetNAS-based architecture search algorithm. Then, the searched light-weight backbone is compared with well-known backbones, such as ResNet, VoVNet, and FBNetV3. In addition, data pertaining to six years of historical forest fire events are employed to estimate the damaged area. Subsequently, a weather API is used to match the recorded events. A Bayesian neural network (BNN) model is used as a regression model to estimate the damaged area. Additionally, the trained model is compared with other widely used regression models, such as decision trees and neural networks. The Faster R-CNN with a searched backbone achieves a mean average precision of 27.9 on 40,000 testing images, outperforming existing backbones. Compared with other regression models, the BNN estimates the damage area with less error and increased generalization. Thus, both proposed models demonstrate their robustness and suitability for implementation in real-world systems. INDEX TERMSForest-fire management, Deep learning, Bayesian neural network, Object detection.

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“…The Cell-DEVS formalism has been used for modeling and simulation of varied phenomena, 8–11 based on our work with Norbert and our original discussions. This non-comprehensive list can be classified in various areas: traffic, 12–16 physics, 17–19 environmental modeling, 19–25 networking, 26–29 architecture and construction, 30–34 biology and medicine, 35–39 defense and emergency planning, 40–48 social models, 48–50 crowd modeling, 51…”

Section: Introductionmentioning

confidence: 99%

Advanced Cell-DEVS modeling applications: a legacy of Norbert Giambiasi

Wainer

2018

SIMULATION

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We describe a number of modeling applications of advanced Cell-DEVS models, a modeling formalism Norbert Giambiasi and I defined in the late 1990s. We discuss improved versions of these models built using the CD ++ toolkit, which was built in order to study, model, and simulate such cellular models. The models have removed some limitations that standard cellular models have, which allow each cell to use multiple state variables and multiple ports for inter-cell communications. We show the application of the formalism in three different areas of science and engineering: social models, pedestrian analysis and occupancy in buildings, and virus spreading in computer networks.

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Event Triggering Estimation for Cell-DEVS: Wildfire Spread Simulation Case

Cited by 5 publications

References 24 publications

Specification of the State's Lifetime in the Devs Formalism by Fuzzy Controller

Specification of the State's Lifetime in the Devs Formalism by Fuzzy Controller

Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data

Advanced Cell-DEVS modeling applications: a legacy of Norbert Giambiasi

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