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<p>With the increasing impacts of global climate change, forest fires pose an increasingly severe threat to ecosystems and human societies. This study delves into the imperative realm of forest fire risk prediction, which is crucial in escalating threats exacerbated by global climate change. This study significantly improves the accuracy and robustness of the predictions by introducing a hybrid model integrating the backpropagation neural network (BPNN) and the genetic algorithm (GA). Compared to traditional neural networks, the genetic algorithm-backpropagation neural network (GA-BP) model exhibits significant advantages in weight optimization, overcoming limitations of BP networks such as slow learning rates and susceptibility to local optima. The experimental results demonstrate that the GA-BP model, under various combinations of neuron counts and activation functions, improves accuracy by nearly 10% and reduces loss values by almost 47%. These outcomes emphasize the effectiveness of the GA-BP model in forest fire risk prediction, providing valuable insights and a robust framework for proactive risk management and ecosystem preservation amidst escalating threats of severe forest fires.</p>

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Climate change impacts on wildfire risk indices forecast based on an improved genetic neural network algorithm: a case study of Guangxi, China

  • 期刊论文

  • 发表日期：

    2025年2月27日

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  • Shuo Zhang,
  • Mengya Pan

摘要

With the increasing impacts of global climate change, forest fires pose an increasingly severe threat to ecosystems and human societies. This study delves into the imperative realm of forest fire risk prediction, which is crucial in escalating threats exacerbated by global climate change. This study significantly improves the accuracy and robustness of the predictions by introducing a hybrid model integrating the backpropagation neural network (BPNN) and the genetic algorithm (GA). Compared to traditional neural networks, the genetic algorithm-backpropagation neural network (GA-BP) model exhibits significant advantages in weight optimization, overcoming limitations of BP networks such as slow learning rates and susceptibility to local optima. The experimental results demonstrate that the GA-BP model, under various combinations of neuron counts and activation functions, improves accuracy by nearly 10% and reduces loss values by almost 47%. These outcomes emphasize the effectiveness of the GA-BP model in forest fire risk prediction, providing valuable insights and a robust framework for proactive risk management and ecosystem preservation amidst escalating threats of severe forest fires.