开发一种新的混合模型,使用人工神经网络和爬行动物搜索算法来增强流量估计
Mohammad Javad Bahmani1, Zahra Kayhomayoon2, Sami Ghordoyee Milan3
1Department of Water Resources Engineering, Faculty of Civil Engineering, Azad University, Tehran, Iran.
Scientific reports
|February 19, 2025
概括
一个新的爬行动物搜索算法 (RSA) 与人工神经网络 (ANN) 结合,改善了伊朗的流量预测. 虽然RSA表现有希望,但人工神经网络加粒子群优化 (ANN-PSO) 模型在水文预测方面取得了卓越的结果.
科学领域:
- 水文和水资源工程 水文和水资源工程
- 环境科学中的人工智能
- 计算流体动力学的流体动力学.
背景情况:
- 精确的流量预测对于水资源管理至关重要,特别是在数据稀缺的地区.
- 人工神经网络 (ANN) 对于时间序列预测是有效的,但需要优化以提高性能.
- 在水文应用中,Metaheuristic算法有可能提高ANN模型的准确性.
研究的目的:
- 使用人工神经网络 (ANN) 模型,预测伊朗乌尔米亚的每月流量.
- 为了评估新的元启发优化器,爬行动物搜索算法 (RSA) 的性能,当与流量预测的ANN相结合时.
- 为了比较ANN-RSA对ANN的有效性,与粒子优化 (PSO) 和鱼优化算法 (WOA) 相结合.
主要方法:
- 使用温度,降水和流量数据开发了五个输入变量模式.
- 训练和测试的ANN模型分别使用70%和30%的数据.
- 实施并比较混合模型:用于流量模拟的ANN-RSA,ANN-PSO和ANN-WOA.
主要成果:
- ANN-RSA混合模型在各种站点和数据模式中展示了有希望的结果,在Band,Babaroud,Nazlo和Tapik站点有着显著的性能指标.
- 在流量模拟准确性方面,ANN-PSO混合模型始终优于ANN-RSA模型.
- 滞后的月流量被确定为影响预测准确性的重要输入参数,尽管模型性能在不同的水文条件和位置上有所不同.
结论:
- 新的爬行动物搜索算法 (RSA) 显示了在流量预测中增强ANN性能的潜力,特别是在特定的水文环境中.
- 与ANN-RSA相比,混合模型,特别是ANN-PSO,在月度流量预测方面提供了更高的准确性.
- 该研究强调了参数选择的重要性,并强调了混合人工智能方法在应对水文挑战方面的潜力.
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