Forest History: International Studies on Socioeconomic and Forest Ecosystem Change

Baker, R. D.

doi:10.1016/s0378-1127(01)00441-8

Cited by 3 publications

(2 citation statements)

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“…Forest resources are some of the most important natural resources on Earth. Not only are they the main component of the Earth's ecosystem, they also provide many resources for human life and production [1]. Forest fires are one of the primary natural disasters that destroy forest resources.…”

Section: Introductionmentioning

confidence: 99%

“…The main objectives of this study are (1) to propose a new definition of wildfire scale that defined by the combination of a fire's duration and the size of the area it burns, (2) to investigate the capability of three neural network models (i.e., backpropagation neural network (BPNN), RNN, and LSTM models) to predict the scale of wildfires in forests using meteorological data, and (3) to provide an efficient estimate of future wildfires' scales. The application of these models is underpinned by real-world data from a fire-prone landscape in Alberta, Canada.…”

Section: Introductionmentioning

confidence: 99%

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A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors

2019

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A forest fire is a natural disaster that destroys forest resources, thus having a severe impact on humans and on the animals and plants that depend on the forest environment. This paper presents a model for predicting the scale of forest wildfires of Alberta, Canada. A fire's scale is determined by the combination of the fire's duration and the size of the area it burns. Our prediction model enables fire rescuers to take appropriate measures to minimize damage caused by a wildfire based on its predicted scale in the fire's early stages. The modeling data were collected from the Canada National Fire Database (CNFDB) published by Natural Resources Canada, which includes wildfire and meteorological data for Alberta, Canada. The size of the burned area and the fire's duration were used to estimate the scale of a wildfire. After multicollinearity testing and feature normalization, the data were divided into training and testing sets. Taking the meteorological factors as input values, a backpropagation neural network (BPNN), a recurrent neural network (RNN), and long short-term memory (LSTM) were implemented to establish prediction models. Of these classification methods, LSTM exhibited the highest accuracy, 90.9%. The results indicate that it is feasible to predict the scale of a forest wildfire at the beginning of its occurrence using meteorological information.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors

2019

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Fundamental and Real-World Challenges in Economics

Helbing

Balietti

2010

SSRN Journal

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In the same way as the Hilbert Program was a response to the foundational crisis of mathematics [1], this article tries to formulate a research program for the socio-economic sciences. The aim of this contribution is to stimulate research in order to close serious knowledge gaps in mainstream economics that the recent financial and economic crisis has revealed. By identifying weak points of conventional approaches in economics, we identify the scientific problems which need to be addressed. We expect that solving these questions will bring scientists in a position to give better decision support and policy advice. We also indicate, what kinds of insights can be contributed by scientists from other research fields such as physics, biology, computer and social science. In order to make a quick progress and gain a systemic understanding of the whole interconnected socio-economic-environmental system, using the data, information and computer systems available today and in the near future, we suggest a multi-disciplinary collaboration as most promising research approach.

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Texture Analysis of Smoke for A Review on Early Forest Fire Analysis

Abirami

Shalini

et al. 2022

IJARSCT

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The environmental challenges facing the planet today haven't been greater or more complex. Global areas covered by forest and concrete woodlands are threatened by natural disasters that have increased considerably over the past decades, both in terms of frequency and magnitude. Large-scale fires are one of the most damaging natural hazards affecting global climate change and life around the world. To minimize their impact on people and nature, it is necessary to adopt effective methods of prevention, warning and response, strategically and closely coordinated. This paper presents an outline of optical remote sensing technologies used in early fire warning systems and supplies an in-depth survey on both flame and smoke detection algorithms employed by each technology. This project gives the prevention of using Temperature detector LM35 and Gas sensor. After the fire is detected, the device makes an alarm sound and automatically turns on the buzzer.

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Forest History: International Studies on Socioeconomic and Forest Ecosystem Change

Cited by 3 publications

References 0 publications

A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors

A Neural Network Model for Wildfire Scale Prediction Using Meteorological Factors

Fundamental and Real-World Challenges in Economics

Texture Analysis of Smoke for A Review on Early Forest Fire Analysis

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