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2 Ecological Engineering & Environmental Technology

2 2025

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Mapping forest fire risk: A comprehensive approach using analytical hierarchy process, geographic information system, and remote sensing integration

Salman Reem, Karouni Ali, Hamadeh Nizar, Rachid Elias

Ecological Engineering & Environmental Technology

2025

Vol. 26, iss. 2

66--85

Wildfire is one of the natural hazards that is escalating globally. While it can cause extensive harm worldwide, significant economic losses, infrastructural damage, and severe social disruption worldwide, the Mediterranean region is vulnerable because of its distinct climate and vegetation patterns. This study uses geospatial technologies and the multi-criteria decision-making method with Analytical Hierarchy Process to assess and map wildfire risk, using different factors like anthropogenic, meteorological and topographic data. The resulting fire risk map categorizes the area into five zones: very high, high, moderate, low, and very low risk. Findings indicate that 34.89% of the area is at moderate risk, 33.45% at high risk, and 7.62% at very high risk. The model’s final susceptibility map was found to be consistent with the historical fire events that occurred in the area of study, demonstrating the efficacy of the approach utilized to identify and map fire risk zones. This model will enhance disaster response capabilities and preparedness through coordination with stakeholders and development of sustainable forest management contingency plans for more resilient communities.

Hybrid model for enhanced rainfall intensity forecasting in the Mediterranean

Hamadeh Nizar, Farhat Zeinab, Rabih Yahia

Ecological Engineering & Environmental Technology

2025

Vol. 26, iss. 2

292--300

Rainfall intensity plays a critical role in shaping environmental outcomes, particularly in climate-sensitive regions like the Mediterranean. Accurate forecasting of rainfall is essential for effective disaster management and climate adaptation strategies, especially as climate change exacerbates the frequency and severity of extreme weather events. This study applies a hybrid model between decision tree to extract best meteorological features that has the ability to influence precipitation, and random forest to predict rainfall intensity. The hybrid model classifies the rainfall intensity into three categories: no rainfall, medium rainfall, and high rainfall. Furthermore, the study investigates the influence of key meteorological attributes on rainfall intensity, identifying the most significant variables and their impact. The model demonstrates good performance, achieving an accuracy 0.90, a low mean squared error (MSE) of 0.09, and an area under the curve (AUC) of 0.97. These results underscore the reliability of hybrid index in rainfall prediction and its potential for integrating meteorological insights into climate-sensitive planning and decision-making.