4 CONCLUSION & RECOMMENDATIONSThis

4 CONCLUSION & RECOMMENDATIONS
This study yielded a fire risk potential map based on a combination of GIS and fuzzy inference engine and by incorporating important human factors which play a role in fire forests. The results show that combination of GIS, fuzzy inference systems and experimental model of can be of great avail as a proposed model for management and prediction of forest fires. In fact, the capability of this model for incorporation and modeling of parameters impacting forest fire risk makes the utilization of this model as a tool for management of forest fires inevitable. As results show, most of the regions exposed to fire risk are located in the vicinity of rivers and roads as places which are frequented by tourists and travellers more than other sites. This map can be utilized for efficient allocation of facilities and fire-fighting resources. Regions with higher risks are of greater priority. Also more patrol resources and watchtowers can be employed in fire- prone seasons in regions with higher risks. With regard to unprotected regions with great potential of fire, access roads can be built to such areas so the transfer time of resources to these regions during fire incidents reaches its minimum. Parameters such as slope, climate, vegetation cover, and distance from rivers, roads and residential areas are taken into consideration in this study. Other parameters such as annual rainfall level, direction and speed of wind, and etc. can also be considered for a better result. The fire risk potential map can even be calculated for various seasons. Factors mentioned in this study take the share of human factor into account more. Risk of natural phenomena such as thunder can also be considered in future studies. Although the mentioned phenomenon has a smaller share of fire incidents occurred to date, it can be studied as one of fire triggers. It is also recommended to use the proposed model for other regions to verify its accuracy for other regions as well.

Hasil (Bahasa Indonesia) 1:

4 kesimpulan & rekomendasiThis study yielded a fire risk potential map based on a combination of GIS and fuzzy inference engine and by incorporating important human factors which play a role in fire forests. The results show that combination of GIS, fuzzy inference systems and experimental model of can be of great avail as a proposed model for management and prediction of forest fires. In fact, the capability of this model for incorporation and modeling of parameters impacting forest fire risk makes the utilization of this model as a tool for management of forest fires inevitable. As results show, most of the regions exposed to fire risk are located in the vicinity of rivers and roads as places which are frequented by tourists and travellers more than other sites. This map can be utilized for efficient allocation of facilities and fire-fighting resources. Regions with higher risks are of greater priority. Also more patrol resources and watchtowers can be employed in fire- prone seasons in regions with higher risks. With regard to unprotected regions with great potential of fire, access roads can be built to such areas so the transfer time of resources to these regions during fire incidents reaches its minimum. Parameters such as slope, climate, vegetation cover, and distance from rivers, roads and residential areas are taken into consideration in this study. Other parameters such as annual rainfall level, direction and speed of wind, and etc. can also be considered for a better result. The fire risk potential map can even be calculated for various seasons. Factors mentioned in this study take the share of human factor into account more. Risk of natural phenomena such as thunder can also be considered in future studies. Although the mentioned phenomenon has a smaller share of fire incidents occurred to date, it can be studied as one of fire triggers. It is also recommended to use the proposed model for other regions to verify its accuracy for other regions as well.