碎片流量量预测模型基于反向传播神经网络,通过改进的鱼优化算法进行优化
Bo Ni1, Li Li1, Hanjie Lin1
1Department of Civil Engineering, Chongqing Three Gorges University, Wanzhou, 404100, Chongqing, China.
PloS one
|April 9, 2024
概括
这项研究引入了一种改进的鱼优化算法 (WOA),以增强反向传播神经网络 (BPNN) 对碎片流量预测. 优化的BPNN表现出卓越的准确性和稳定性,即使数据有限,识别滑坡沉积物作为关键因素.
科学领域:
- 地质科学和环境科学 地球科学和环境科学
- 计算科学和机器学习
背景情况:
- 碎片流对山区构成重大威胁,需要准确的体积预测以减轻灾害.
- 传统的反向传播神经网络 (BPNNs) 显示出不稳定性和不准确性,对于碎片流预测的有限数据集.
研究的目的:
- 使用优化后传神经网络开发一个更准确,更稳定的碎片流量量预测模型.
- 调查影响地震影响地区碎片流量量的关键因素.
主要方法:
- 改进的鱼优化算法 (WOA),结合立方图和适应性重量调整,用于优化BPNN重量和值.
- 分析了龙门山地区60个碎片流沟,以确定影响因素.
- 优化的BPNN模型经过训练,并与支持矢量机器回归,XGBoost以及由人工蜂群和灰狼算法优化的BPNN进行了验证.
主要成果:
- 来自共地震滑坡的松散沉积物被确定为影响碎片流量的主要因素.
- 优化的BPNN实现了0.193的平均绝对百分比误差,平均绝对误差为29.197 × 10^4 m3,R2为0.912.
- 该模型表现出更高的准确性和稳定性,特别是在不足和复杂的数据集的情况下.
结论:
- 立方图和适应性重量调整优化的WOA显著改善了BPNN在碎片流量量预测方面的性能.
- 开发的模型为在山区和易地震地区的碎片流风险评估和预防策略提供了可靠的工具.
- 这项研究为应用先进的机器学习技术来预测自然灾害提供了宝贵的见解.
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