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使用哈里斯·霍克斯优化-反向传播神经网络模型的中长期区域水需求预测

Mengzhuo Yang¹, Erkun Gao², Gaoxu Wang³

¹Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing, 210029, China. yangmengzhuo0927@hhu.edu.cn.

Scientific reports

|November 12, 2024

概括

准确的水需求预测对于资源管理至关重要. 一个新的哈里斯·霍克斯优化-反向传播神经网络 (HHO-BPNN) 模型在预测区域用水需求方面取得了97%的准确性,超过了传统方法.

科学领域:

环境科学环境科学
水资源管理水资源管理
环境建模中的人工智能

背景情况:

有效的区域水资源分配需要精确的中长期水需求预测.
传统的预测方法经常与水需求因素的复杂性和维度相斗争.

研究的目的:

引入和评估一个与哈里斯·霍克斯优化-反向传播神经网络 (HHO-BPNN) 模型相结合的新型模型,用于中长期的水需求预测.
评估HHO-BPNN的预测性能与既有预测技术相比.
确定影响区域水需求的关键驱动因素.

主要方法:

开发了一种新的HHO-BPNN合模型,用于预测水需求.
应用主要组件分析 (PCA) 用于降低水需求影响因素的维度.
在五个城市使用滑窗方法对1,3年和5年的水需求预测.
使用平均平方误差 (MSE),平均绝对百分比误差 (MAPE),平均绝对误差 (MAE) 和确定系数 (R2) 评估模型性能.

主要成果:

与BPNN,支持矢量机和灰色预测模型相比,HHO-BPNN模型表现出更高的性能.
在农业,工业,家庭和生态水需求类别中实现了高预测精度,总精度为97%.
预测显示与当地城市发展计划有很强的一致性,表明实际效用.

结论:

HHO-BPNN模型为精确的区域水需求预测提供了一个有效的工具.
该研究提供了对水需求的主要驱动因素的见解,支持知情的水资源管理和决策.
该模型的高精度和与发展计划的一致性突出显示了其在城市规划和水资源管理中的实际应用潜力.

关键词:

反向传播神经网络的神经网络.哈里斯·霍克斯优化算法优化算法地区水需求预测预测

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