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1.
Environ Monit Assess ; 195(2): 294, 2023 Jan 12.
Artigo em Inglês | MEDLINE | ID: mdl-36633718

RESUMO

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. The map is divided into five sectors, i.e., minimum, low, moderate, high, and maximum risk of fire. The validation of the fire zoning map was evaluated at 0.83 and the RMSE error was 0.75. The results obtained show that 20% of the area under study is within the average risk category, 11% is within the high-risk category, and 10% is within the very high-risk category of a potential fire hazard. The most important variables were distance from a flowing source, i.e., river or stream, the land formation type, elevation, and the minimum temperature. The identification of suitable locations for firefighting stations was carried out by merging the fuzzy inference system model and Arc GIS, and the results obtained defined 16 possible locations. It was concluded that the application of hybrid models when dealing with the aforementioned variables is effective when seeking to determine locations for the establishment of firefighting stations and rural safety services; moreover, such hybrid models are highly efficacious for determining of fire hazard zones. It is proposed that hybrid models be applied on a large scale for the prevention, control, and management of fires throughout the country.


Assuntos
Incêndios Florestais , Animais , Animais Selvagens , Irã (Geográfico) , Monitoramento Ambiental/métodos , Florestas , Redes Neurais de Computação
2.
Environ Monit Assess ; 191(3): 138, 2019 Feb 07.
Artigo em Inglês | MEDLINE | ID: mdl-30734095

RESUMO

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. Recently, the biodiversity and sustainability of wildlife populations, as well as preserving the integrity of protected areas in the Khuzestan province, have been at risk due to several factors; therefore, the evaluation of management effectiveness in these areas is necessary. The studied areas in this research are protected areas in Khuzestan province, with a history of more than 5 years of management. The results of this study show that Dez, a protected area with the highest points in the planning (38.5), has the highest score in management effectiveness (128.78). Also, Shimbar, a protected area with the lowest score (11), has the lowest score of management effectiveness (64.66) among the other areas. The overall management level of the protected areas in the Khuzestan province is at the low-intermediate managerial level compared to the global average. Therefore, it is necessary to change the policies and management of these areas.


Assuntos
Conservação dos Recursos Naturais/métodos , Ecossistema , Biodiversidade , Monitoramento Ambiental , Irã (Geográfico)
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