农性能指标的变选择最佳的新混合算法来预测地下水位 (案例研究:塔布里兹平原,伊朗)
Mohsen Saroughi1, Ehsan Mirzania2, Mohammed Achite3
1Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
Environmental monitoring and assessment
|February 2, 2024
概括
预测地下水位对于水资源管理至关重要. 一个新的混合算法将人工神经网络与Coot和Honey Badger优化相结合,显著提高了地下水位预测的准确性.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 环境科学中的人工智能
- 超学优化算法 超学优化算法
背景情况:
- 预测地下水位 (GWL) 对水资源管理至关重要,特别是在干旱地区.
- 准确的GWL预测有助于可持续的供水和干旱缓解战略.
- 现有的预测模型需要增强,以提高可靠性.
研究的目的:
- 开发和评估一种新的混合算法,用于预测地下水位.
- 为了比较人工神经网络 (ANN) 与Coot和Honey Badger优化算法的性能.
- 通过使用香农来确定评估模型性能最有效的统计指标.
主要方法:
- 开发了一个混合模型,将ANN与Coot和Honey Badger优化算法集成在一起.
- 气象数据 (温度,蒸发,降水),过去的GWL和时间数据被用作输入.
- 香农被用来评估44个模型性能评估的统计指标.
- 模型的性能使用诸如Akaike信息标准 (AIC) 和余平方和 (RSS) 等指标进行分析.
主要成果:
- 混合算法在GWL预测中显著优于独立的ANN模型.
- 与Coot优化算法 (COOT-ANN) 相比,蜜优化算法 (HBA-ANN) 显示出更高的性能.
- 根据香农,AIC和RSS被确定为最好的准确度和错误指标.
- HBA-ANN获得了最低的AIC值 (-344),显示出最好的预测准确度.
结论:
- 拟议的混合元启发算法为准确的地下水位预测提供了强大的方法.
- 蜜优化算法对于增强基于ANN的GWL预测非常有效.
- 这项研究为优化水资源管理的GWL预测模型提供了宝贵的见解.
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