集成深度学习和改进的多目标算法,以优化水库运行,平衡人类和下游生态需求
Rujian Qiu1, Dong Wang1, Vijay P Singh2
1Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, PR China.
Water research
|February 18, 2024
概括
本研究介绍了一种多目标的生态调度模型,以平衡水电发电与生态流量和水温需求. 该模型优化了水库运营,以改善环境效应和水电效益.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 生态生态学 生态生态学
背景情况:
- 的运行大大改变了下游流量和水温,影响了水生生态系统和鱼类息地.
- 为水力发电和生态需求平衡水库运行提出了复杂的挑战.
- 现有的战略往往缺乏对经济和生态目标之间的权衡的全面分析.
研究的目的:
- 为水库运营制定一个多目标的生态调度模型.
- 同时考虑水电发电,生态流动和生态水温需求.
- 分析水库管理中的竞争目标之间的权衡.
主要方法:
- 使用混合长短期内存和1D卷积神经网络 (LSTM_1DCNN) 进行大排放温度模拟.
- 开发了一个改进的epsilon连续域 (ε-MOACOR) 多目标殖民地优化算法.
- 将一个集成的多目标模拟优化 (MOSO) 框架应用于三峡水库.
主要成果:
- 与其他模型相比,LSTM_1DCNN在预测水排放温度方面表现优越.
- 经济 (水力发电) 和生态目标之间存在着显著的冲突.
- ε-MOACOR 算法有效地解决了冲突,并在优化任务中显示出高效率.
- 莫索框架为各种水文年产生了务实的帕雷托最佳解决方案.
- 排放量增加或排放分布不均改善了生态水温保证指数.
结论:
- 开发的多目标生态调度模型为平衡水库运行提供了一个强大的框架.
- LSTM_1DCNN和e-MOACOR模型为生态模拟和优化提供了先进的工具.
- 水库运行战略必须整合生态水温考虑,以实现可持续管理.
- 优化水库运营可以减轻负面环境影响,同时支持水电效益.
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