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[Forest lighting fire forecasting for Daxing'anling Mountains based on MAXENT model].

Yu Sun, Ming-Chang Shi, Huan Peng

    Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
    |July 12, 2014
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    Summary

    A MAXENT model can predict forest lightning fires in Daxing'anling Mountains. Key factors include daily rainfall, cloud-to-ground lightning count, and current intensity, achieving moderate prediction accuracy.

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    Area of Science:

    • Forestry
    • Environmental Science
    • Geospatial Analysis

    Background:

    • Daxing'anling Mountains face frequent forest lightning fires.
    • Accurate lightning fire prediction is crucial for this region.

    Purpose of the Study:

    • To develop and evaluate a MAXENT model for predicting forest lightning fires in Daxing'anling.
    • Identify key environmental variables influencing lightning fire occurrence.

    Main Methods:

    • Utilized MAXENT (Maximum Entropy) modeling approach.
    • Assessed environmental variable importance using training gain and Jackknife.
    • Evaluated model accuracy with Kappa and AUC values.
    • Conducted collinearity diagnostics using Variance Inflation Factor (VIF).

    Main Results:

    • Daily rainfall, cloud-to-ground lightning count, and current intensity were identified as the most significant predictors.
    • Lightning energy and neutralized charge showed collinearity, excluding them from model training.
    • Model accuracy improved with increased test data proportion, achieving average Kappa of 0.772 and AUC of 0.859.

    Conclusions:

    • The MAXENT model demonstrates moderate prediction accuracy for forest lightning fires in Daxing'anling.
    • The model can be a valuable tool for forest fire management and prevention in the region.