基于长短期记忆神经网络耦合物理约束的库湖系统多目标智能防洪调度规则提取

IF 5 2区 地球科学 Q1 WATER RESOURCES
Bin Xu , Xinman Qin , Huili Wang , Xuesong Yang , Jianyun Zhang , Fubao Yang , Jiaying Tan , Jiayi Jiang , Pengwei Jiang , Yutong Chen , Wei Zhi , Shanshui Yuan
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引用次数: 0

摘要

研究区域:长江下游地区巢湖流域。研究重点提出了一种将长短期记忆网络与物理约束相结合的库湖系统防洪多目标智能操作规则提取方法。该方法扩展了模型的输入因子,实现了多目标非劣解集的预测,并将水文物理约束纳入模型的损失函数中。这种耦合方法既提高了模型的物理可解释性,又提高了库湖系统多目标非劣解的预测能力,使水库、湖泊和防洪控制点的洪水指标最小。结果表明,与传统LSTM相比,CP-LSTM (Physical Constrained Long - Short-Term Memory,物理约束长短期记忆)模型具有以下优势:(1)CP-LSTM的均方根误差降低0.33 %,Nash-Sutcliffe效率提高1.12 %,表明预测建模决策变量的准确性有所提高。显著提高了峰值流量预测精度,改善了LSTM模型低峰值流量预测精度的局限性;(2)在客观空间预测方面,目标值误差最大降低8.00 %,预测目标空间与真实目标空间的面积重叠率提高21.50 %。通过将水文物理约束整合到深度学习框架中,并将其扩展到多目标决策问题中,为基于人工智能的防洪规则提取及其在多目标洪水调度策略中的应用提供了一种新颖有效的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-objective intelligent flood control operation rules extraction for reservoirs-lake system based on long and short-term memory neural networks coupled with physical constraints

Study region

Chaohu Basin, lower Yangtze River region, China.

Study focus

This study proposes a multi-objective intelligent operation rules extraction method for flood control in reservoirs-lake system, which integrates Long Short-Term Memory (LSTM) networks with physical constraints. The method extends the input factors of the model to realize the prediction of multi-objective non-inferior solutions set and incorporates hydrological physical constraints into the model’s loss function. This coupling method improves both the model’s physical interpretability and the predictive ability in yielding multi-objective non-inferior solutions over reservoirs-lake system, which minimizes flood indices on reservoirs, lake and flood control point.

New hydrological insights for the region

The results show that compared to the conventional LSTM, the CP-LSTM (Physical Constrained Long Short-Term Memory) model demonstrates the following advantages: (1) The CP-LSTM reduces Root Mean Square Error by 0.33 % and increases Nash-Sutcliffe efficiency by 1.12 %, indicating improved accuracy in prediction modeling decision variables. Moreover, it significantly enhances peak flow prediction accuracy, improving the limitation of low peak flow prediction accuracy in the LSTM model; (2) In terms of objective space prediction, the maximum reduction in Objective Values Error reaches 8.00 %, while the Area Overlap Ratio of the predicted objective space to the true objective space improves by 21.50 %. By integrating hydrological physical constraints into the deep learning framework and extending it to multi-objective decision-making problems, this study provides a novel and effective approach for Artificial Intelligence -based flood control rules extraction and its application in multi-objective flood operation strategies.
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来源期刊
Journal of Hydrology-Regional Studies
Journal of Hydrology-Regional Studies Earth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)
CiteScore
6.70
自引率
8.50%
发文量
284
审稿时长
60 days
期刊介绍: Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.
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