使用社会经济和气候因素与ELM优化进行精确的水需求预测的混合方法
Zhaohui Li1, Gang Wang2, Danfeng Lin2
1School of Foreign Languages, Changchun University of Finance and Economics, Changchun, 130122, Jilin, China.
Heliyon
|February 7, 2024
概括
这项研究引入了一种混合模型,将社会经济和气候数据结合起来,以准确预测水需求. IANA-ELM模型显示出强大的预测准确性,有助于水资源管理.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 机器学习应用 机器学习应用
背景情况:
- 准确的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社会经济和气候因素之间的复杂相互作用.
- 整合不同的数据源可以提高预测准确性.
研究的目的:
- 开发和评估用于预测水需求的混合方法.
- 整合社会经济 (人口,人均国内生产总值) 和气候 (温度,降水) 变量.
- 使用改进的巢算法优化预测模型.
主要方法:
- 使用极端学习机器 (ELM) 进行预测建模.
- 采用了改进的巢算法来优化ELM参数 (权重和偏差).
- 使用根平均平方误差 (RMSE) 和相关系数 (R2) 评估模型性能.
主要成果:
- 混合IANA-ELM模型实现了对水需求的高预测精度.
- 训练数据中的R2值在0.607 (温度) 到0.708 (降雨) 之间.
- 测试数据中的R2值在0.592 (温度) 到0.708 (降雨) 之间,表明性能强.
结论:
- 混合方法有效地通过整合社会经济和气候因素来预测水需求.
- IANA-ELM模型显示了改善水资源管理策略的巨大潜力.
- 准确的水需求预测支持政策制定者和水利公司的知情决策.
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