Using neural networks and a fuzzy inference system to evaluate the risk of wildfires and the pinpointing of firefighting stations in forests on the northern slopes of the Zagros Mountains, Iran (case study: Shimbar national wildlife preserve).

Predicting potential fire hazard zones in natural areas is one of the means of mitigating and managing fires. The current research focuses on the prioritizing of elements which contribute to the spread of fire and the special zoning of potentially dangerous areas in addition to the pinpointing of locations for the establishment of fire stations in forested areas in the Shimbar national reserve based on historical data spanning 2001 to 2018. The study utilizes elements (physiological, vegetation cover, meteorological, anthropological factors) contributing to wildfires as inputs into an artificial neural network and the development of a fuzzy inference system in order to produce fire zoning maps for the region under study.

Management effectiveness and conservation prioritizing the protected areas using RAPPAM methodology (case study: Khuzestan province).

In the world today, where the industrialization of countries is still on the increase, protecting habitats and wildlife will be possible only in protected areas. That is why maintaining species' diversity and preventing the destruction of habitats in protected areas has been of great interest. Rapid Assessment and Prioritization of Protected Area Management methodology is one of the most common methods of management effectiveness assessment and is used as a tool for managers and decision-makers of protected areas.