Artificial intelligence for forest fire prediction

Sakr, George E.; Elhajj, Imad H.; Mitri, George; Wejinya, Uchechukwu C.

doi:10.1109/aim.2010.5695809

Cited by 70 publications

(47 citation statements)

References 8 publications

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“…Sakr et al (2010) proposed a forest fire risk prediction algorithm based on Support Vector Machines. The algorithm predicts the fire hazard level of the day from previous weather condition.…”

Section: Forest Wildfire Predictionmentioning

confidence: 99%

A Literature Review: Forest Management with Neural Network and Artificial Intelligence

Imada

2014

Communications in Computer and Information Science

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Abstract. The 8th International Conference on Neural Network and Artificial Intelligence invites a plenary talk whose topic is Forest Resource Maintenance. It's not specifically from the point of information technology but from a general point of view. Thinking of the title of this conference, 'neural networks and artificial intelligence,' the talk follows this literature review on the topic of forest management by neural network and/or artificial intelligence. While focus is mainly put on wildfire prediction, also preservation of biodiversity of forest ecosystems and forest resource management are surveyed. Comparison of these methods with traditional statistical methods such as regression is mentioned too.

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Section: Forest Wildfire Predictionmentioning

confidence: 99%

A Literature Review: Forest Management with Neural Network and Artificial Intelligence

Imada

2014

Communications in Computer and Information Science

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“…According to Sakr, G.E et al [40], Support Vector Machine (SVM) is a machine learning algorithm that uses a linear hyperplane to create a classifier with a maximal margin. For cases where the data is not linearly separable, the SVM maps the data into a higher dimensional space called the feature space.…”

Section: Support Vector Machinementioning

confidence: 99%

“…We base our prediction models on a continuous observation of a number of specific features [40]. The following initial features are selected for the three target values (CPU utilization, response time and throughput) [2]:…”

Section: Feature Selectionmentioning

confidence: 99%

“…The importance of selecting the right features for prediction modeling is very critical to reducing the potential source of error as the data mining principle of "junk in, junk out" means erroneous predictions can occur even if the prediction algorithm is optimal [40]. The Weka tool [23] is used to determine the relevance of each feature in an instance to the target class (CPU, response time and throughput).…”

Section: Feature Reductionmentioning

confidence: 99%

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Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment

Ajila

Bankole

2016

TMLAI

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In order to meet Service Level Agreement (SLA) requirements, efficient scaling of Virtual Machine (VM) resources in cloud computing needs to be provisioned ahead due to the instantiation time required by the VM. One way to do this is by predicting future resource demands. The existing research on VM resource provisioning are either reactive in their approach or use only non-business level metrics. In this research, a Cloud client prediction model for TPC-W benchmark web application is developed and evaluated using three machine learning techniques: Support Vector Regression (SVR), Neural Networks (NN) and Linear Regression (LR). Business level metrics for Response Time and Throughput are included in the prediction model with the aim of providing cloud clients with a more robust scaling decision choice. Results and analysis from the experiments carried out on Amazon Elastic Compute Cloud (EC2) show that Support Vector Regression provides the best prediction model for random-like workload traffic pattern.

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“…Logistic regression has been used to model fire ignition probability (Vasconcelo et al 2001;Rollins et al 2004;Martinez et al 2009;Jurdao et al 2012;Sitanggang et al 2013;Eskandari & Chuvieco 2015), and the artificial neural network has been used to predict fire regimes (Alonso-Betanzos et al 2002;Vakalis et al 2004, Vasilakos et al 2009Satir et al 2016). Finally, the support vector machine approach has also been proposed for fire risk modelling (Cortez & Morais 2007;Sakr & Elhajj 2010).…”

Section: Introductionmentioning

confidence: 99%

Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran

Eskandari

Miesel

2017

Geomatics, Natural Hazards and Risk

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This study was done to evaluate the efficiency of three methods to predict the high-risk areas for fire in District Three of Neka Zalemroud forests located in Mazandaran Province, Iran. The fuzzy analytic hierarchy process (fuzzy AHP) and the spatial correlation method were used to model fire risk in the study area. The Dong model was used to provide the fire risk map. Following the construction of fire risk maps using three methods, the map of actual fires was overlaid to validate the used methods. Then the area of the high-risk and very high-risk classes of each fire risk map within the perimeters of actual fires was calculated. Results showed that the high-risk areas in the fire risk map prepared by the fuzzy AHP and spatial correlation methods closely follow the actual fires. On the other hand, the high-risk areas in the fire risk map prepared by the Dong model showed only a moderate agreement with actual fire areas. The final results showed that the fuzzy AHP model (accuracy 0.8) and the spatial correlation model (accuracy 0.92) have the strongest ability to predict the fire high-risk areas in Hyrcanian forests of Iran, relative to the Dong model (accuracy 0.51).

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Artificial intelligence for forest fire prediction

Cited by 70 publications

References 8 publications

A Literature Review: Forest Management with Neural Network and Artificial Intelligence

A Literature Review: Forest Management with Neural Network and Artificial Intelligence

Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment

Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran

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