[1]施景山,唐开云,沈巧能,等.树突白箱神经网络的水压预测方法优化与应用[J].中国给水排水,2026,42(3):59-64.
　SHIJingshan,TANGKaiyun,SHENQiaoneng,et al.Optimization and Application of a Water Pressure Prediction Method Based on Dendrite Net[J].China Water & Wastewater,2026,42(3):59-64.
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树突白箱神经网络的水压预测方法优化与应用

中国给水排水[ISSN:1000-4062/CN:12-1073/TU] 卷: 第42卷 期数: 2026年第3期 页码: 59-64 栏目: 出版日期: 2026-02-01
Title:
Optimization and Application of a Water Pressure Prediction Method Based on Dendrite Net
作者:
施景山1， 唐开云2， 沈巧能2， 赵克琼2， 李文品2， 谢明军2， 杜坤1
（1.昆明理工大学 建筑工程学院，云南 昆明 650500；2.昆明市盘龙区水务局，云南 昆明 650000）
Author(s):
SHI　Jingshan1， TANG　Kaiyun2， SHEN　Qiaoneng2， ZHAO　Keqiong2， LI　Wenpin2， XIE　Mingjun2， DU　Kun1
（1. Faculty of Civil Engineering and Mechanics， Kunming University of Science and Technology， Kunming 650500， China； 2. Water Affairs Bureau of Panlong District， Kunming 650000， China）
关键词:
供水管网;  水压监测点;  水压预测;  树突白箱神经网络;  智慧水务
Keywords:
water supply network;  water pressure monitoring point;  water pressure prediction;  dendrite net;  smart water
摘要:
针对城市供水管网水压预测精度与效率不足的问题，引入树突白箱神经网络（DD）进行优化。传统的人工神经网络（ANN）存在“黑箱”性强、计算复杂等局限，而DD模型能够通过显式表达输入输出关系，简化运算并提高预测精度。基于L-TOWN管网的案例研究表明，DD模型在预测精度、稳定性和计算效率方面均优于ANN模型，尤其在需水量波动情境下表现稳健，且无需重复训练，有利于实时监测。
Abstract:
Dendrite net (DD) was proposed as an optimization solution to address the issue of inadequate accuracy and efficiency in predicting water pressure within urban water supply networks. Traditional artificial neural network (ANN) exhibits limitations, including a pronounced “black box” characteristic and computational complexity. In contrast, the DD model enhances operational efficiency and prediction accuracy by explicitly representing the input-output relationship. The case study conducted on the L-TOWN pipe network demonstrated that the DD model outperformed the ANN model in prediction accuracy, stability, and computational efficiency. Notably, under conditions of fluctuating water demand, the DD model maintained stable performance without the need for repeated retraining, thereby facilitating real?time monitoring applications.

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更新日期/Last Update: 2026-02-01