Predicting wildfires in Algerian forests using machine learning models

Zaïdi, Abdelhamid

doi:10.1016/j.heliyon.2023.e18064

Cited by 23 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resulting maps revealed distinct patterns in the ZGI for each year, consistent with previous studies, that underscore the significant influence of climate conditions, such as rainfall and temperature, on the vegetation cover in semi-arid and semi-Mediterranean regions [30,34,65,66]. The ZGI thresholds been defined using the Natural Breaks (NB) method, which is based on actual values in the dataset rather than using predefined intervals [43].…”

Section: Discussionsupporting

confidence: 79%

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

Rahimi,

Duarte,

Teodoro

2024

Sustainability

View full text Add to dashboard Cite

Annually, the oak forests of the Zagros Mountains chains in western Iran and northeastern Iraq face recurring challenges posed by forest fires, particularly in the Kurdo–Zagrosian forests in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation conditions. Integrating in situ data, Remote Sensing (RS) data, and Geographical Information Systems (GIS) integration presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces and tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies by isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas crucial for the study region’s fire susceptibility assessment. Results demonstrate a substantial overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

show abstract

Section: Discussionsupporting

confidence: 79%

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

Rahimi,

Duarte,

Teodoro

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Step 1: In the data pre-processing, based on the dimensionality requirements of onedimensional CNN, we binarized and incremented the dimensionality of the dataset. To ensure a balanced distribution and avoid an excessive number of similar samples after splitting, we utilized the train_test_split function with the shuffling option set to True and a fixed random_state value [66][67][68]. For the fire probabilities obtained from the test group (30%).…”

Section: Pso-cnn Methodsmentioning

confidence: 99%

“…In order to get details about the impact of different risk factor on the model prediction [67], we used shapely values (SHAP) to quantitatively evaluate the contribution of the input factors to the prediction of PSO-CNN model. For this technique, the core idea is to compute the marginal contribution of features to the model output, which is widely used in the literature [67].…”

Section: Evaluation Metricsmentioning

confidence: 99%

A PSO-CNN-Based Deep Learning Model for Predicting Forest Fire Risk on a National Scale

You,

Zheng,

Yang

et al. 2023

Forests

View full text Add to dashboard Cite

Forest fires have a significant impact on terrestrial ecosystems, leading to harm to biodiversity and environment. To mitigate the ecological damage caused by forest fires, it was necessary to develop prediction models of fire risk. In this study, by evolving the optimal architecture and parameters using the particle swarm optimization (PSO) algorithm, a convolutional neural network (CNN) deep learning model was proposed to predict forest fire risk on a national scale. Utilizing fire data and fire risk factors from 2001 to 2020 in China, the PSO-CNN-based deep learning model (PSO-CNN) was utilized and tested. Compared to logistic regression, random forest, support vector machine, k-nearest neighbors, and CNN models, the PSO-CNN model exhibited superior performance with an accuracy of 82.2% and an AUC value of 0.92. These results clearly highlighted the effectiveness of the PSO-CNN model in enhancing the accuracy of forest fire prediction. Furthermore, the forest fire risk prediction level estimated by the proposed model on a national scale for the entire country was mostly consistent with actual fire data distribution, indicating its potential to be used as an important direction for deep learning in forest fire prediction research.

show abstract

“…While study [23] applied artificial neural networks to predict forest fires in embedded devices using collected meteorological data from wireless sensor networks, nine machine learning algorithms were investigated and compared based on the obtained results, and they propose an embedded forest fire prediction model. The study [24] addresses wildfire prediction using a recent dataset from 2012, employing an artificial neural network (ANN) that outperforms other classifiers in accuracy, precision, and recall. Key features influencing predictions include relative humidity (RH), drought code (DC), and initial spread index (ISI).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Forest Fire Prediction: A Case Study in North Algeria

Ghibeche,

Sellam,

Nouri

et al. 2024

ISI

View full text Add to dashboard Cite

Wildland fires are the most common peril for forests due to climate change. Furthermore, it is an uncontrollable disaster and poses a great deal of threat to human health and ecosystems. In Algeria, almost 40,000 hectares are burned each year, approximately 1% of all existing woodlands of the country. In this work, the forest fire event prediction is highlighted using machine learning. The study utilized data sets from several sources, including fire data obtained from the fire information system for resource management by NASA (FIRMS) and climate data accessed from the NASA energy project API, derived from the MODIS satellite (NASA forecasting of energy resources around the world). Fire data from NASA provides real-time information, spanning from 2000 to 2020. The methodology process of creating the prediction system involved collecting the data, preprocessing the data, finding the best models, training and testing the models, and evaluating them for validation. The machine learning model was trained and validated using 70% and 30% of the set features with a performance accuracy of up to 86%. Upon completion, we deployed our selected machine learning model to create a Web platform enables different end users to check possible future forest fires by select a geographical area on a world map. The objective of our machine learning model is to analyze the weather data of the selecting area on the map in real time and predict whether a fire will occur or not. This prediction system will enhance early detection, allowing prompt response measures to be implemented, reducing the risk of uncontrolled wildfires and safeguarding ecosystems and communities.

show abstract

Predicting wildfires in Algerian forests using machine learning models

Cited by 23 publications

References 18 publications

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

Zagros Grass Index—A New Vegetation Index to Enhance Fire Fuel Mapping: A Case Study in the Zagros Mountains

A PSO-CNN-Based Deep Learning Model for Predicting Forest Fire Risk on a National Scale

Machine Learning for Forest Fire Prediction: A Case Study in North Algeria

Contact Info

Product

Resources

About