By Arun Pratap Singh
Dehradun, 15 Apr: The Uttarakhand Forest Department has planned an innovative initiative to develop a prediction model for forest fires. This model, to be driven by artificial intelligence, would aim to forecast potential incidents during the forest fire season with high accuracy. A robust database system will support the model, ensuring predictions are grounded in reliable information. This was shared with Garhwal Post by Additional Principal Chief Conservator of Forests (APCCF) Nishant Verma.
Verma shared that the forest department has established an Integrated Command and Control Centre, which would store extensive historical data as database. This includes details of areas most affected by forest fires over the last decade, ranges where such incidents are either steadily increasing or consistently occurring, and the classifications of forests most vulnerable to fires. These data parameters are instrumental in enhancing future planning.
Nishant Verma also shared that the Integrated Command and Control Centre is central to this endeavour. With its vast repository of data history, the department is preparing a prediction model using artificial intelligence to improve readiness in sensitive areas. This model will allow the department to channel its efforts effectively towards regions prone to forest fires. The proposed prediction model seeks to analyse patterns, such as the yearly rise in forest fire incidents or the intervals between occurrences. By leveraging this information, the department would develop a comprehensive plan for tackling fires, identifying priority zones, and deploying resources efficiently.
For an effective use of this model, the department would focus more on sensitive areas. Upon successful implementation, the prediction model is expected to enable the department to concentrate its resources on high-risk areas. This would not only aid in meticulous planning but also ensure the optimal utilisation of forest personnel and the fortification of vulnerable regions. However, initial studies are essential to validate the accuracy of the data and the effectiveness of the artificial intelligence-driven model.
