对SWAT和ANN技术的相互比较,用于模拟上印度河上方阿斯托尔盆地的流动
Sunaid Khan1, Afed Ullah Khan2, Mehran Khan2
1National Institute of Urban Infrastructure Planning, University of Engineering and Technology, Peshawar 25000, Pakistan
概括
人工神经网络 (ANN) 模型在模拟巴基斯坦阿斯托尔盆地每月的河流流量方面超过了土壤和水评估工具 (SWAT). 安恩准确地预测了一般和极端的流量条件,为水文预测提供了卓越的工具.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 环境建模环境建模
- 环境科学中的人工智能
背景情况:
- 精确的流量模拟对于数据稀缺地区的水资源管理至关重要.
- 阿斯托尔盆地由于地形和气候复杂,在水文预测方面面临挑战.
- 像SWAT这样的传统水文模型在捕捉极端流动事件方面可能存在局限性.
研究的目的:
- 模拟和比较土壤和水评估工具 (SWAT) 和人工神经网络 (ANN) 的性能,用于每月的流量预测.
- 在校准和验证期间使用统计指标评估这两种模型的准确性.
- 确定最适合在阿斯托尔盆地进行流量预测的模型.
主要方法:
- 来自阿斯托尔盆地的每月流量历史记录被用于模拟.
- 用土壤和水资源评估工具 (SWAT) 来进行水文建模.
- 开发并测试了一种具有 (2,27,1) 架构的人工神经网络 (ANN) 模型.
- 模型性能使用确定系数 (R2),纳什-萨特克利夫效率 (NSE),百分比偏差 (PBIAS) 和根-平均-平方误差 (RMSE) 来评估.
主要成果:
- 在基于统计指标的校准和验证期间,ANN模型在SWAT模型中表现优越.
- 与SWAT.相比,ANN获得了较高的R2 (0.88校准,0.86验证) 和NSE (0.82校准,0.95验证) 值.
- SWAT显示低估了高流量和高估了低流量,而ANN有效地捕获了一般和极端流量条件.
结论:
- 与SWAT相比,人工神经网络 (ANN) 模型是一个更准确,更可靠的工具,用于在阿斯托尔盆地进行月度流量模拟.
- 由于ANN能够准确预测极端流量条件,因此对水资源管理和决策非常有价值.
- 建议进行进一步的研究,将其他机器学习模型与SWAT进行比较,以提高该地区的流量预测准确度.
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