预测城市用水需求使用不同的基于混合的元启发算法激发了提取人工神经网络超参数的灵感
Salah L Zubaidi1,2, Hussein Al-Bugharbee3, Ali W Alattabi4
1Department of Civil Engineering, Wasit University, Wasit, 52001, Iraq. salahlafta@uowasit.edu.iq.
Scientific reports
|October 14, 2024
概括
这项研究引入了一种新的方法,用于利用天气数据和人工神经网络 (ANN) 预测城市用水需求. 先进的PSOGA-ANN模型在准确预测水需求方面表现出卓越的表现.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 计算智能是一种计算智能.
背景情况:
- 准确的城市用水需求预测对于可持续的水资源管理至关重要.
- 传统的方法往往难以捕捉气象变量和水消耗之间的复杂关系.
研究的目的:
- 开发和评估一种用于量化城市用水需求的新方法.
- 通过集成先进的数据处理和人工智能来提高水需求预测的准确性.
主要方法:
- 利用数据预处理技术的组合来提高输入数据的质量.
- 采用一个人工神经网络 (ANN),优化了基因算法,启用了粒子群优化 (PSOGA) 算法.
- 将PSOGA-ANN模型与各种元启发算法进行比较,包括修改PSO和标准PSO.
主要成果:
- 所有模型都基于气象变量充分模拟了每月的城市用水需求.
- 统计学适应性测量证实,PSOGA-ANN模型显著超过了基准测试算法.
- 有效的数据处理提高了初始数据的质量,并优化了对城市用水需求的预测.
结论:
- 拟议的PSOGA-ANN方法为城市用水需求预测提供了强大而准确的方法.
- 优化数据处理和先进的算法是提高水资源管理策略可靠性的关键.
- 这项研究为水利公司提供了一个有价值的工具,以更好地管理供需.
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