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在多个地区计量区的水需求预测,基于多级校正模块的神经网络架构

Qidong Que^1,2, Jinliang Gao^1,2, Yizhou Qian³

¹School of Environment, Harbin Institute of Technology, Harbin 150090, China.

Water research X

|December 2, 2024

概括

本研究引入了一种新型的神经网络,用于在多个地区计量区 (DMA) 中进行短期水需求预测 (STWDF). 该模型通过利用DMA之间的相关性和多变量校正,显著提高了预测准确性.

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
城市水系统工程城市水系统工程

背景情况:

准确的短期水需求预测 (STWDF) 对于高效的城市供水网络管理至关重要.
预测单个地区计量区 (DMA) 的需求比总需求更为不确定.
现有的模型经常与多DMA用水固有的空间和时间相关性作斗争.

研究的目的:

开发一种创新的神经网络架构,用于跨多个DMA的同时STWDF.
通过结合多变量校正和DMA之间的相关性来提高预测准确性.
为简化多DMASTWDF和改进城市水资源管理战略提供统一的框架.

主要方法:

开发一个多级校正模块神经网络,将长短期记忆 (LSTM) 和卷积神经网络 (CNN) 与注意力机制相结合.
该模型在意大利北部的10个DMA的每小时STWDF一个星期的应用.
使用多变量校正和分析DMA之间的相关性和气象特征的影响.

主要成果:

与传统的门式循环单元 (GRU) 或LSTM模型相比,拟议的模型在评估指标中显示出5%-20%的平均性能改善.
在单个DMA,总用水需求和极端条件预测场景中始终实现了卓越的准确性.
解释性分析证实了模型的可行性,并强调了气象数据对特定DMA的预测贡献.

结论:

新的多尺度校正模块神经网络为城市水网提供了STWDF的显著进步.
利用DMA之间的相关性和多变量校正是提高预测准确度和有效管理水资源的关键.
统一的输入-输出框架简化了多DMA STWDF,为未来的研究和实际应用提供了宝贵的见解.

关键词:

注意力机制注意力机制卷积神经网络是一个卷积神经网络.深度学习中的解释性长期短期记忆神经网络的神经网络短期对水需求的预测

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