Hoping the best, expecting the worst: Forecasting forest fire risk in Algeria using fuzzy logic and GIS

IF 2.7 Q1 FORESTRY
Louiza Soualah , Abdelhafid Bouzekri , Haroun Chenchouni
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引用次数: 0

Abstract

Forest fires pose severe threats to ecosystems and communities globally, especially in vulnerable semi-arid regions like North Africa. Understanding the key factors influencing forest fire dynamics is essential for effective management and mitigation. This study aims to comprehensively analyze forest fire risk patterns in Djebel El Ouahch's massif (Algeria), focusing on integrating bioclimatic, fuel, geomorphological, and human factors through advanced fuzzy logic and geographic information system (GIS) techniques. Climatic station data, satellite imagery, and GIS were employed to map bioclimatic parameters, land cover, and geomorphological features. Fuzzy logic systems were applied to integrate these factors, assigning appropriate weights based on their significance. The resulting forest fire prediction model was defuzzified to generate predictive maps indicating varying vulnerability levels within the study area. Predictive maps delineated areas of low to high forest fire risk. Low-risk zones were characterized by sparse vegetation, while high-risk regions featured densely vegetated slopes near human settlements. The study identified critical factors influencing vulnerability, emphasizing the impact of climate, terrain, and human activities. Urgent attention was directed toward high-risk areas, necessitating tailored fire prevention measures and strategic urban planning to minimize human-induced risks. The results underscored the complex interaction of natural and anthropogenic factors in shaping forest fire susceptibility. Understanding these dynamics facilitates evidence-based policymaking, enhancing forest fire preparedness, biodiversity preservation, and community safety. Additionally, the study emphasized the need for continuous research incorporating real-time climate data and socio-economic factors to refine predictive models. This research provided valuable insights into forest fire risk patterns in Djebel El Ouahch, serving as a foundation for targeted fire management strategies. By bridging the gap between theoretical knowledge and practical application, this study contributes significantly to sustainable forest management and disaster mitigation efforts globally, emphasizing the importance of proactive measures in safeguarding vulnerable ecosystems and communities.

Abstract Image

希望最好,期待最坏:利用模糊逻辑和地理信息系统预测阿尔及利亚森林火灾风险
森林火灾对全球生态系统和社区构成严重威胁,尤其是在北非等脆弱的半干旱地区。了解影响森林火灾动态的关键因素对于有效管理和缓解森林火灾至关重要。本研究旨在全面分析阿尔及利亚 Djebel El Ouahch 丘陵的森林火灾风险模式,重点是通过先进的模糊逻辑和地理信息系统(GIS)技术,综合考虑生物气候、燃料、地貌和人为因素。利用气候站数据、卫星图像和地理信息系统绘制生物气候参数、土地覆盖和地貌特征图。应用模糊逻辑系统整合这些因素,并根据其重要性分配适当的权重。对由此产生的森林火灾预测模型进行模糊化处理,以生成显示研究区域内不同脆弱程度的预测地图。预测地图划定了从低到高的森林火灾风险区域。低风险区域的特点是植被稀疏,而高风险区域的特点是靠近人类居住区的山坡上植被茂密。研究确定了影响脆弱性的关键因素,强调了气候、地形和人类活动的影响。对高风险地区的关注刻不容缓,有必要采取有针对性的防火措施和战略性城市规划,以最大限度地降低人为风险。研究结果表明,自然和人为因素在影响森林火灾易发性方面存在复杂的相互作用。对这些动态因素的了解有助于循证决策,加强森林防火准备、生物多样性保护和社区安全。此外,该研究还强调了结合实时气候数据和社会经济因素进行持续研究以完善预测模型的必要性。这项研究为了解 Djebel El Ouahch 的森林火灾风险模式提供了宝贵的见解,为制定有针对性的火灾管理策略奠定了基础。这项研究弥补了理论知识与实际应用之间的差距,为全球可持续森林管理和减灾工作做出了重要贡献,强调了采取积极措施保护脆弱生态系统和社区的重要性。
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来源期刊
Trees, Forests and People
Trees, Forests and People Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
4.30
自引率
7.40%
发文量
172
审稿时长
56 days
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