Journal Article

Advancing wildfire prediction in Nepal using machine learning algorithms

Kuinkel, Dipesh, Aryal, Deepak, Pokharel, Binod, Sapkota, Saugat, Wu, Yanhong, Wang, S-Y Simon, Bing, Haijian, Bhandari, Biplov, Kuikel, Sajesh, Marahatta, Suresh, Joshi, Khagendra Prasad

2025

Overview

Wildfires are increasingly threatening Nepal, particularly during the dry pre-monsoon months (March-May), leading to severe ecological impacts and disruptions to local communities. To improve wildfire prediction and preparedness, this study evaluated four advanced machine learning algorithms—Random Forest, Radial Basis Function Neural Network, Artificial Neural Network, and Support Vector Machine—using comprehensive dataset (2001–2023) of meteorological, topographical, anthropogenic, locational, and vegetation variables. The Random Forest (RF) model outperformed others, achieving the highest accuracy (88.6%) and predictive reliability (AUC: 0.96). Notably, vapor pressure deficit emerged as the strongest predictor, contrasting previous studies where precipitation was often considered dominant. Utilizing the robust RF model, a high resolution (1-km) wildfire risk map identified 11.1% of Nepal, encompassing 12 districts and 48 municipalities primarily in the southwestern region, as very high-risk areas. By integrating daily meteorological data into wildfire predictions, this research provides an innovative framework that enhances risk management strategies, offering actionable insights for decision-makers and supporting resilience-building efforts in fire prone regions.

• Publisher: IOP Publishing
• Subject: Algorithms / Artificial neural networks / Decision trees / Environmental risk / fire management / fire prediction / Human influences / Learning algorithms / Local communities / Machine learning / Meteorological data / Nepal / Neural networks / Predictions / Radial basis function / Random variables / Risk management / risk mapping / Support vector machines / Vapor pressure / wildfire / Wildfires