Fire is among major threats to the world’s forests, leading to tremendous biodiversity losses. Forest fire in India has greatly increased in the last few decades; the state of Arunachal Pradesh, a recognized Himalayan biodiversity hotspot, is extremely prone to this disaster. The objective of this study was to develop GIS integrated mapping of direct and indirect factors, leading to a predictive model to identify settlements/villages for the strategic allocation of resources towards damage mitigation and control. Initial hotspots were generated by integrating factors of socio-economy, geography and climate, using differential weightage. The intersection of these with fire data, led to identification of final hotspots within the predictive model. The model was improved by linking it to a mobile App and the WebGIS portal. Of the 5258 settlements/villages, a total of 560 were found to be at high fire risk. Percentage correlation increased from 63 to 74, after data revision through the App. A focused intervention on predicted villages was undertaken, resulting in a decrease of 31% of fire incidence in comparison of last five years (2015–20) data. Such advanced information about fire disaster with optimal use of limited resources was greatly helpful, and helped protect the rich Himalayan biodiversity.

火灾是对世界森林的重大威胁之一，导致生物多样性遭受巨大损失。在过去的几十年中，印度的森林大火大量增加；阿鲁纳恰尔邦（Arunachal Pradesh）州是公认的喜马拉雅生物多样性热点地区，极易发生这场灾难。这项研究的目的是开发直接和间接因素的GIS集成映射，从而形成一种预测模型，以识别定居点/村庄，以战略性地分配资源以减轻和控制损害。最初的热点是通过使用不同的权重综合社会经济，地理和气候因素而产生的。这些与火灾数据的交集导致在预测模型中识别出最终热点。通过将模型链接到移动应用程序和WebGIS门户对该模型进行了改进。在5258个定居点/村庄中，共有560个被发现存在火灾的高风险。通过App修改数据后，百分比相关性从63增加到74。对预测的村庄进行了重点干预，与最近五年（2015-20年）的数据相比，导致火灾发生率降低了31％。这些关于火灾的先进信息，可以最大限度地利用有限的资源，极大地帮助了人们，并帮助保护了喜马拉雅山丰富的生物多样性。