使用基于子搜索算法的支向量机器模型预测悬浮沉积物负载
Sandeep Samantaray1, Abinash Sahoo2, Deba Prakash Satapathy2
1Department of Civil Engineering, National Institute of Technology Srinagar, Hazratbal, Jammu and Kashmir, 190006, India.
Scientific reports
|June 5, 2024
概括
一个新的支持向量机器与搜索算法 (SVM-SSA) 模型准确地预测了河流中的悬浮沉积物负载 (SSL). 这种人工智能方法为水文建模和水资源管理提供了可靠和高效的解决方案.
科学领域:
- 环境工程 环境工程
- 水文学的水文学
- 人工智能的人工智能
背景情况:
- 悬浮沉积物负载 (SSL) 预测对于水文建模和水资源工程至关重要.
- 沉积物运输复杂且非线性,受雨水,流量强度和沉积物供应的影响.
- 人工智能 (AI) 为水资源工程中的多方面的问题提供了先进的解决方案.
研究的目的:
- 开发一个强大的支持向量机器与搜索算法 (SVM-SSA) 模型,用于悬浮沉积物负载 (SSL) 预测.
- 评估SVM-SSA模型与其他混合模型和基准SVM模型的性能.
- 用MAE,RMSE,R2和ENS等指标来评估模型的准确性.
主要方法:
- 提出了一个新的SVM-SSA模型,用于在布拉曼尼河流域的SSL计算.
- 考虑了五种不同的模型开发场景,包括滞后沉积物和排放数据.
- 将SVM-SSA与SVM-BOA,SVM-GOA,SVM-BA以及传统的SVM模型进行比较.
主要成果:
- SVM-SSA模型在预测SSL方面表现出很高的准确性,特别是在V场景 (沉积物和排放的3个月滞后).
- 在RMSE=15.5287,MAE=15.3926和ENS=0.96481.1的情况下实现了卓越的性能.
- 传统的SVM模型产生了最差的结果,突出了拟议的AI方法的有效性.
结论:
- SVM-SSA模型是一种精确可靠的方法,用于模拟河流中悬浮沉积物负载.
- 开发的模型满足了实际工程应用的精度要求.
- 这种方法显著减少了计算时间,同时确保了高预测精度.
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