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通过将卫星数据与新型混合深度学习和黑寡妇优化算法集成来评估泛加拿大野火易感性

Khabat Khosravi¹, Ashkan Mosallanejad², Sayed M Bateni³

¹Department of Natural Resources, College of Agriculture and Natural Resources, Razi University, Kermanshah, Iran; School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PE, Canada.

The Science of the total environment

|April 16, 2025

概括

这项研究开发了新的深度学习模型,以预测加拿大各地的野火概率. 混合BiLSTM-BWO模型实现了最高的准确性,识别了改善野火管理的关键风险因素.

科学领域:

环境科学环境科学
计算机科学计算机科学
遥感遥感遥感遥感

背景情况:

越来越多的野火频率和社会生态影响需要改进预测方法.
准确地绘制野火发生地图对于有效的土地管理和缓解战略至关重要.

研究的目的:

开发和评估新的深度学习模型,用于加拿大全国范围的野火概率预测.
整合遥感数据,深度学习算法和元启发式优化,以提高野火预测.

主要方法:

使用黑寡妇优化器 (BWO) 开发并混合了长短期记忆 (LSTM),循环神经网络 (RNN),双向LSTM (BiLSTM) 和双向RNN (BiRNN) 模型.
利用4240个历史野火地点 (2014-2023) 和14个与野火相关的预测因素进行模型培训和测试.
通过基尼系数和模型性能通过接收器操作特征曲线 (AUC) 下的面积和统计错误指标评估预测因素的重要性.

主要成果:

混合BiLSTM-BWO模型表现出卓越的性能,AUC为0.9686,超过了其他开发的模型.
约有14.5%和9.8%的加拿大被分类为分别具有高和非常高的野火易感性.
风速,土地使用/土地覆盖,降水,特定湿度和最高温度被确定为最有影响力的预测因素.

结论:

混合深度学习模型,特别是BiLSTM-BWO,对于加拿大的野火预测和易感性映射非常有效.
调查结果为全国范围内加强野火预防,减轻和土地管理策略提供了宝贵的见解.
该研究强调了将先进的计算技术与遥感集成为环境风险评估的潜力.

关键词:

双向的长期短期记忆.黑寡妇优化器的优化器是黑寡妇.加拿大加拿大加拿大加拿大加拿大加拿大敏感性映射的绘制.野火大火在野火大火

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