A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems

Abid, Faroudja

doi:10.1007/s10694-020-01056-z

Cited by 120 publications

(56 citation statements)

References 55 publications

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“…For analysis purposes, we divided this period by decades. Period one (2001( -2011( ) and Period two (2012( -2021. The average number of publications in period one was two.…”

Section: Publicationsmentioning

confidence: 99%

“…Therefore, there has been extensive research on the combination of different methods to development more robust and powerful models which has improved the accuracy of forest fire models (Abedi Gheshlaghi et al 2021;Eskandari et al 2020;Tien Bui Dieu et al 2019;Tuyen et al 2021;Razavi-Termeh et al 2020;Tehrany et al 2019). Moreover, in recent years essential literature reviews have been conducted which provide a better understanding of the forest fire modelling approaches, their implementation and challenges in different domains of forest fire research (Naderpour et al 2019;Jain et al 2020;Abid 2021). Although, these reviews have provided essential insights on forest fire modelling methods, they have placed very little emphasis on the factors that are used as inputs into the models and the actors (countries and authors) that are conducting this type of research.…”

Section: Introductionmentioning

confidence: 99%

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Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review

Chicas

Nielsen

2022

Nat Hazards

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In the last decades, natural fire regimes have experienced significant alterations in terms of intensity, frequency and severity in fire prone regions of the world. Modelling forest fire susceptibility has been essential in identifying areas of high risk to minimize threats to natural resources, biodiversity and life. There have been significant improvements in forest fire susceptibility modelling over the past two decades 2001–2021. In this study, we conducted a systematic literature review of literature covering forest fire susceptibility modelling published during this period. The review provides insights on the main themes of forest fire susceptibility modelling research, the main base input factors used in models to map forest fire susceptibility, the main researchers, the areas where this type of research were implemented, technology and models used. It also highlights collaboration opportunities, and regions, such as Central America and Africa, where mapping of forest fire susceptibility is needed. We argue that such knowledge is crucial in order to identify critical factors and opportunities which can aid in improving factor selection and forest fire management.

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“…For analysis purposes, we divided this period by decades. Period one (2001( -2011( ) and Period two (2012( -2021. The average number of publications in period one was two.…”

Section: Publicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review

Chicas

Nielsen

2022

Nat Hazards

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“…BPNN (Back Propagation) was proposed by Rumelhart et al, 1986. Its multi-layer neuron structure and transfer function give BPNN powerful nonlinear learning ability [6,7] . Based on the robustness, it can meet the need of prediction accuracy when the relationship between input factors and output factors is unknown.…”

Section: Introductionmentioning

confidence: 99%

Multifactor optimization of MICP base on BP model

Zhuo

Fan

et al. 2022

J. Phys.: Conf. Ser.

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Microbial-induced calcite precipitation (MICP) can be used to cement soil and produce new biomaterials. The formation of this material is affected by many factors, such as physical, chemical, biological factors, many studies focused on the effect of a single factor on the strength ignoring the synergy between factors. Back-propagation neural network (BPNN) can be used as a multi-factor nonlinear prediction model and an analysis method. 140 MICP grout tests in the literature were summarized to15 factors affecting UCS and act as BPNN input data. At last, five key factors were elected based on weight analysis of well-trained BPNN. On this basis, a simple strength model composed of those factors is established, which can well predict the strength of MICP grouting soil with practical convenience. Key factors and strength prediction models help popularize MICP for engineering applications and optimize grouting experiments.

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“…For example, ground-based sensors can complement satellite sensing technology when cloud cover and smoke may interfere with defining the position and velocity of the fire *Correspondence should be addressed to: Peter L. Woodfield, E-mail: p.woodfield@griffith.edu.au Fire Technology Ó 2022 The Author(s) Manufactured in The United States https://doi.org/10.1007/s10694-022-01224-3 front. Multiple data sources can be integrated through machine learning algorithms [12,13].…”

Section: Introductionmentioning

confidence: 99%

Underground LoRa Sensor Node for Bushfire Monitoring

Herring

Sharp²,

Roberts

et al. 2022

Fire Technol

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The feasibility of an underground LoRaWAN bushfire temperature sensing node from the point of view of survivability in a bushfire event is considered. Thermal penetration into the soil is modelled using a one-dimensional analytical formulation for a semi-infinite solid. A working prototype of the sensor unit was tested experimentally beneath a relatively small (400 mm × 400 mm base) timber fire. A buried LoRa radio (depth 100 mm) with a k-type thermocouple monitored the temperature from beneath the fire. The analysis demonstrates that under dry soil conditions, the time that the sensor node under the fire base can endure is proportional to the square of the depth of burial of the electronic components and inversely proportional to the thermal diffusivity of the soil. The original contribution of the work is in the practical demonstration of the durability of a LoRa sensing node beneath a fire front for bushfire sensing applications.

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A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems

Cited by 120 publications

References 55 publications

Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review

Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review

Multifactor optimization of MICP base on BP model

Underground LoRa Sensor Node for Bushfire Monitoring

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