使用神经网络和灰狼优化算法预测物种分布的新型混合模型
Hao-Tian Zhang1, Ting-Ting Yang1, Wen-Ting Wang2
1School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730030, People's Republic of China.
Scientific reports
|May 20, 2024
概括
一个新的混合算法,灰狼优化器算法优化反向传播神经网络 (GNNA),改进了物种分布建模. GNNA显示出卓越的预测准确性,特别是在数据有限的入侵物种.
科学领域:
- 生态生态学 生态生态学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 反向传播神经网络 (BPNN) 用于物种分布建模,但面临参数设置挑战.
- 在BPNN中优化连接重量对于精确的物种分布模拟至关重要.
- 像GBM,GLM,MaxEnt和RF这样的现有模型在物种分布预测方面具有不同的性能.
研究的目的:
- 通过将灰狼优化器 (GWO) 与BPNN集成来开发一种新的混合算法,GNNA,以提高物种分布预测.
- 通过使用多种评估指标,评估GNNA与已建立的物种分布模型 (SDM) 的性能.
- 评估GNNA在预测入侵植物物种潜在的非本地分布方面的有效性.
主要方法:
- 开发了灰狼优化器算法,通过将GWO的全球搜索功能纳入BPNN,优化了反向传播神经网络 (GNNA).
- 将GNNA与通用推进模型 (GBM),通用线性模型 (GLM),最大 (MaxEnt) 和随机森林 (RF) 进行比较.
- 接收器运行特征曲线 (AUC) 下的利用面积,科恩的卡帕和真实技能统计 (TSS) 用于对23种物种的模型评估.
主要成果:
- 与标准BPNN相比,GNNA显示了显著改善的预测性能.
- GNNA的表现优于GLM和GBM,并显示了与MaxEnt和RF相似的结果,特别是在小样本尺寸的情况下.
- 在预测入侵植物物种的潜在分布方面,GNNA证明非常有效.
结论:
- 将GWO与BPNN集成后,为物种分布建模提供了一个强大而准确的工具 (GNNA).
- GNNA为现有的SDM提供了强大的替代方案,特别是在处理有限的数据时.
- GNNA显示出生态应用的巨大潜力,包括对入侵物种的管理.
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