新型人工蜂群优化ANN和数据预处理技术的应用,用于每月流量估计
Okan Mert Katipoğlu1, Mehdi Keblouti2, Babak Mohammadi3
1Erzincan Binali Yıldırım University, Faculty of Engineering and Architecture, Department of Civil Engineering, Erzincan, Türkiye. okatipoglu@erzincan.edu.tr.
概括
精确的流量估计对于水资源管理至关重要. 这项研究引入了新的混合模型,将人工蜂群优化的人工神经网络与信号分解技术相结合,以改善水文预测.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 环境科学中的计算智能
背景情况:
- 准确的流量估计对于可持续的水资源管理,防灾和各种与水有关的应用至关重要.
- 传统的水文模型往往在准确预测河流流量方面面临挑战,特别是在容易发生干旱和洪水等极端事件的地区.
研究的目的:
- 开发和评估用于增强流量估计的新型混合模型.
- 评估结合人工蜂群 (ABC) 优化的人工神经网络 (ANN) 与先进的信号分解技术的有效性.
主要方法:
- 开发了一个人工蜂群-人工神经网络 (ABC-ANN) 混合模型.
- 集成了ABC-ANN模型与局部平均分解 (LMD) 和完整集体实证模式分解与自适应噪声 (CEEMDAN) 信号分解技术.
- 应用这些混合模型 (LMD-ABC-ANN和CEEMDAN-ABC-ANN) 在东黑海地区,土耳其进行流量预测.
主要成果:
- 该研究成功评估了新的LMD-ABC-ANN和CEEMDAN-ABC-ANN混合方法的性能.
- 证明了这些先进的混合模型在提高流量预测准确度方面的潜力.
结论:
- 开发的混合模型为增强流量估计提供了可靠的策略.
- 这些发现为水资源规划者和政策制定者提供了有价值的资源,帮助他们有效地管理水资源.
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