The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires and their movement, and a hybrid strategy for wildfire control and geostatistics was developed to evaluate the impact on forests. The various methods we included herein are those based on information, such as knowledge-based AHP-crisp for figuring out forest-fire risk, using such variables as forest type, topography, land-use and land cover, geology, geomorphology, settlement, drainage, and road. The models for forest-fire ignition, progression, and action are built on various spatial scales, which are three-dimensional layers. To create a forest fire risk model using three different methods, a study was made to find out how much could be lost in a certain amount of time using three samples. Precedent fire mapping validation was used to produce the risk maps, and ground truths were used to verify them. The accuracy was highest in the form of using “knowledge base” methods, and the predictive value was lowest in the use of an analytic hierarchy process or AHP (crisp). Half of the area, about 53.92%, was in the low-risk to no-risk zones. Very-high- to high-risk zones cover about 24.66% of the area of the Sirmaur district. The middle to northwest regions are in very-high- to high-risk zones for forest fires. These effects have been studied for forest fire suppression and management. Management, planning, and abatement steps for the future were offered as suitable solutions.

Wildfire is one of the complex and damaging natural phenomena in the world. Wildfires pose an enormous challenge to predict and monitor complicated integration chemistry with the physical aspects of solid-gas stage combustion and heat transmission spatially diverse vegetations, topography, and detailed time and space conditions at various spatial and time scales. The research community has greatly enhanced its efforts in the last 25 years to better understand wildfires by improving observation, measurement, analysis and modelling. The fast development of spatial data analysis and computer technology has been facilitated. This combination allowed new decision promotion systems, information collection, analysis methods, growth, and existing fire management instruments. In several countries, despite this activity, forest fires remain a serious problem. Factors that raise the world risk of wildfires are climate change, urban-rural migration and the creation of the interface between urban and wildlands. These events demonstrate the tremendous destructive force of wildfires of great magnitude, sometimes well beyond our concrete containment and control capability. In addition to firefighters, foresters and other organised systems, the scientific community is key to addressing the problems of fire recognition in the countryside. Advances in our understanding of fire-fighting mechanisms and the relationship between fire activity and the natural and constructed environment can lead to successful fire risk decision support systems, the predictions for fire propagation and the reduction of fire risk. The convergence of forest ecosystems and forest fires has become the growing threat posed by human influences and other factors to ecosystems, resources and even human lives. Climate change will change forest fire regimes to enhance forest fire understanding and to build strategies for mitigation and adaptation. The study highlights broad aspects of forest fire in combination with