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Academic literature on the topic 'Forest fire forecasting Australia Mathematical models'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Forest fire forecasting Australia Mathematical models"

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Wang, Xiaoxue, Chengwei Wang, Guangna Zhao, Hairu Ding, and Min Yu. "Research Progress of Forest Fires Spread Trend Forecasting in Heilongjiang Province." Atmosphere 13, no. 12 (December 16, 2022): 2110. http://dx.doi.org/10.3390/atmos13122110.

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In order to further grasp the scientific method of forecasting the spreading trend of forest fires in Heilongjiang Province, which is located in Northeast China, the basic concepts of forest fires, a geographical overview of Heilongjiang Province, and an overview of forest fire forecasting are mainly introduced. The calculation and computer simulation of various forest fire spread models are reviewed, and the selected model for forest fires spread in Heilongjiang Province is mainly summarized. The research shows that the Wang Zhengfei–Mao Xianmin model has higher accuracy and is more suitable for the actual situation of Heilongjiang Province. However, few studies over the past three decades have updated the formula. Therefore, this empirical model is mainly analyzed in this paper. The nonlinear least squares method is used to re-fit the wind speed correction coefficient, which gets closer results to the actual values, and the Wang Zhengfei–Mao Xianmin model is rewritten and evaluated for a more precise formula. In addition, a brief overview of the commonly used Rothermel mathematical–physical model and the improved ellipse mathematical model is given, which provides a basis for the improvement of the forest fires spread model in Heilongjiang Province.
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Meraliyev, Bakhtiyor Askarovich, and Kurmangazy Sakenuly Kongratbayev. "Applying machine learning models for predicting forest fires in Australia and the influence of weather on the spread of fires based on satellite and weather forecast data." Proceedings of International Young Scholars Workshop 9 (June 8, 2020). http://dx.doi.org/10.47344/iysw.v9i0.187.

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What shall we expect from the year 2020? The coronavirus pandemic is not the worst thing that humanity can face in the near future. According to the observations of the scientists, in March, 2020, the planet temperature warmed up to the record-high level. Also, the temperature of the world’s oceans exceeded its average temperatures by 80%, and prognosis of the meteorological observations is not good. The warming seas had already led to catastrophic disaster. The average temperature increase can also lead to hurricanes, drought, invasion of locusts and, the worst, to forest fires. Natural disasters lead to loss of life, destruction of properties and infrastructure, loss of animal natural habitats, displacement of humans. And the results of these all lead to humanitarian catastrophes, including social and economic.The situations related to the nature are always very serious, as the whole world is involved. This is like butterfly effect, i.e., the natural disaster in Australia affect the economic and ecologic situation in USA and England. Taking the Australia, they faced problem that cannot be avoided. Nevertheless, the world can be prepared and prevent from the huge disasters. The forecasting of forest fires can really be helpful, as well as the inquiry of the weather impact on fires. The current paper is focused on the study of fire forecasting and weather influence on fire. The relevance of the study is important, as the global warming and human caused fires are increasing and there is a trend that Australia’s fires became more dangerous and longer lasting. The artificial intelligence, particularly machine learning algorithms, can help to make appropriate calculations and predictions to safe the ecosystem and human lives.According to the preliminary research results we acquire; in-depth multidimensional analysis confirms almost 100 percent dependence of bushfires on the weather conditions. Using the machine learning algorithms, it would be possible to predict the time and positioning of inflammation source.
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Dissertations / Theses on the topic "Forest fire forecasting Australia Mathematical models"

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Beck, Judith A. "Decision support for Australian fire management." Master's thesis, 1988. http://hdl.handle.net/1885/155786.

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Books on the topic "Forest fire forecasting Australia Mathematical models"

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Sikkink, Pamela G. Field guide for identifying fuel loading models. Fort Collins, CO: United States Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2009.

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Sikkink, Pamela G. Field guide for identifying fuel loading models. Fort Collins, CO: United States Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2009.

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Wagner, C. E. Van. Equations and FORTRAN program for the Canadian Forest Fire Weather Index System. Ottawa: Canadian Forestry Service, 1985.

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Martell, David Leigh. Development of mathematical models for predicting daily people-caused forest fire occurence in Ontario. Toronto: Faculty of Forestry, University of Toronto, 1985.

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Wilson, Ralph A. A theoretical basis for modeling probability distributions of fire behavior. Ogden, Utah: Intermountain Research Station, 1987.

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Latham, Don J. Ignition probabilities of wildland fuels based on simulated lightning discharges. Ogden, UT (324 25th, Ogden 84401): U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1989.

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Latham, Don J. Ignition probabilities of wildland fuels based on simulated lightning discharges. [Ogden, Utah]: U.S. Dept. of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, 1989.

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Lawson, Bruce D. Diurnal variation in the fine fuel moisture code: Tables and computer source code. Victoria, B.C: Canada-British Columbia Partnership Agreement on Forest Resource Development, 1996.

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Book chapters on the topic "Forest fire forecasting Australia Mathematical models"

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Lyell, Christopher Sean, Usha Nattala, Rakesh Chandra Joshi, Zaher Joukhadar, Jonathan Garber, Simon Mutch, Assaf Inbar, et al. "A forest fuel dryness forecasting system that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models." In Advances in Forest Fire Research 2022, 21–27. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_1.

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Accurate and timely forecasting of forest fuel moisture is critical for decision making in the context of bushfire risk and prescribed burning. The moisture content in forest fuels is a driver of ignition probability and contributes to the success of fuel hazard reduction burns. Forecasting capacity is extremely limited because traditional modelling approaches have not kept pace with rapid technological developments of field sensors, weather forecasting and data-driven modelling approaches. This research aims to develop and test a 7-day-ahead forecasting system for forest fuel dryness that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models. The integrated system was established across a diverse range of 30 sites in south-eastern Australia. Fuel moisture was measured hourly using 10-hour automated fuel sticks. A subset of long-term sites (5 years of data) was used to evaluate the relative performance of a selection of machine learning (Light Gradient Boosting Machine (LightGBM) and Recurrent Neural Network (RNN) based Long-Short Term Memory (LSTM)), statistical (VARMAX) and process-based models. The best performing models were evaluated at all 30 sites where data availability was more limited, demonstrating the models' performance in a real-world scenario on operational sites prone to data limitations. The models were driven by daily 7-day continent-scale gridded weather forecasts, in-situ fuel moisture observation and site variables. The model performance was evaluated based on the capacity to successfully predict minimum daily fuel dryness within the burnable range for fuel reduction (11 – 16%) and bushfire risk (
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Bacciu, Valentina, Maria Mirto, Sandro Luigi Fiore, Costantino Sirca, Josè Maria Costa Saura, Sonia Scardigno, Valentina Scardigno, et al. "An operational platform for fire danger prevention and monitoring: insights from the OFIDIA2 project." In Advances in Forest Fire Research 2022, 87–92. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_13.

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The project OFIDIA2 (Operational FIre Danger preventIon plAtform 2), funded by the Interreg Greece-Italy 2014-2020 Programme, proposed a pragmatic approach to improve the operational capacity of the stakeholders to detect and fight forest wildfires. A data analytics system was designed and implemented within the project to manage, transform, and extract knowledge from heterogenous data sources, through forecasting models such as weather, fire danger, and fire behaviour models. The high-resolution weather forecasting network previously developed in OFIDIA1 was enhanced by using a mesoscale configuration of the WRF-ARW model over the Central Mediterranean Sea. A nested domain over the Southern Italy at ~2km horizontal resolution allows getting high-resolution weather forecasts (2x2km) and processing data into fire danger models. Fires, fuel, topography and weather data were collected from several sources and used to run and calibrate fire models (FlamMap and Wildfire Analyst) in Apulia region (Italy). Based on the analyses of recurrent weather conditions leading to large fires, fire metrics’ maps for prevention and fire-fighting activities were produced. Finally, a Decision Support System (DSS) was also developed to provide support for 1) the selection of fire behaviour scenarios by means of mathematical models; and 2) the prevention of emergencies thanks to weather forecast information with fire danger indices at high resolutions.
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