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<p>Forest fires pose significant ecological, social, and economic threats, necessitating accurate risk assessment to inform disaster management. This study integrates geospatial technology with machine learning to map forest fire risk in Hamirpur District, Himachal Pradesh, India, using the knowledge-based analytical hierarchy process (AHP) and artificial neural network (ANN) models. Eleven variables, including elevation, slope, temperature, and proximity to roads and settlements, were analyzed to generate forest fire hazard maps. Demographic data of vulnerable groups were incorporated to delineate risk zones. The ANN model outperformed AHP, achieving an accuracy of 68.23% with an area under the curve (AUC) of 0.746. The results indicate that 77.66% of the district falls within low to no-risk zones, 13.86% is at moderate risk, and 8.13% is at high risk. These findings support targeted fire prevention strategies, enhancing regional resilience and sustainable forest management in alignment with SDG-15 (Life on Land).</p>

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Integration of Machine Learning into Geospatial Modelling for Forest Fire Risk Assessment in Hamirpur District, Himachal Pradesh (India)

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Prachismita Nayak,
  • Shruti Kanga,
  • Suraj Kumar Singh,
  • Gowhar Meraj,
  • Pankaj Kumar,
  • Saurabh Kumar Gupta,
  • Bhartendu Sajan

摘要

Forest fires pose significant ecological, social, and economic threats, necessitating accurate risk assessment to inform disaster management. This study integrates geospatial technology with machine learning to map forest fire risk in Hamirpur District, Himachal Pradesh, India, using the knowledge-based analytical hierarchy process (AHP) and artificial neural network (ANN) models. Eleven variables, including elevation, slope, temperature, and proximity to roads and settlements, were analyzed to generate forest fire hazard maps. Demographic data of vulnerable groups were incorporated to delineate risk zones. The ANN model outperformed AHP, achieving an accuracy of 68.23% with an area under the curve (AUC) of 0.746. The results indicate that 77.66% of the district falls within low to no-risk zones, 13.86% is at moderate risk, and 8.13% is at high risk. These findings support targeted fire prevention strategies, enhancing regional resilience and sustainable forest management in alignment with SDG-15 (Life on Land).