考虑到水量和水质目标的适应性水库运行:应用并行蜂自动机和次季节性流量预测
Yasaman Kazemnadi1, Mahta Nazari2, Reza Kerachian3
1Research Associate, School of Civil Engineering, College of Engineering, University of Tehran., Tehran, Iran.
Journal of environmental management
|February 10, 2024
概括
本研究引入了利用天气预报来管理水量和水质的适应性水库运行框架. 这种新的方法显著减少了计算时间,并实现了高精度,证明了其实际适用性.
科学领域:
- 水资源管理水资源的管理.
- 环境工程环境工程
- 计算水文学计算水文学
背景情况:
- 传统的水库运营往往难以有效地平衡水量和水质目标.
- 准确预测流入量及其对水质的影响对于最佳的水库管理至关重要.
- 现有的优化模型可能是计算密集型,限制了它们的实时应用.
研究的目的:
- 开发和评估一个新的水库运营适应性框架,整合水量和水质管理.
- 通过利用缩小规模和偏差校正的降水预报来提高水库运行的效率和准确性.
- 通过伊朗卡克赫水的案例研究来评估拟议框架的性能.
主要方法:
- 使用欧洲中期天气预报中心 (ECMWF) 的降水预报 (最多6个月的预期时间) 缩小和偏差校正.
- 采用降雨流失模拟模型来预测季节性水库流入.
- 整合了对概率存储曲线的长期优化模型,并与CE-QUAL-W2水质模型连接,用于自适应模拟-优化.
- 实现了并行蜂自动机与本地搜索 (PCA-LS) 优化算法,以应对计算挑战.
主要成果:
- 与非主导排序遗传算法II (NSGA-II) 相比,PCA-LS算法显示计算时间减少了95%.
- 适应性优化模型实现了1.68%的精度,与使用观察到的流入数据的完美模型差异.
- 该框架成功评估了水库内和流出水的质量,考虑了多个操作门.
结论:
- 拟议的适应式水库运行框架有效平衡了水量和水质目标.
- PCA-LS优化算法显著提高了计算效率,使实时自适应操作成为可能.
- 该框架的高准确性和效率,由卡尔克赫水案例研究验证,支持其广泛适用于可持续水库管理.
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