相关实验视频
Updated: May 8, 2025

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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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城市水能消耗预测受气候变化影响,使用创新的深度学习方法
Dianli Wang1, Yu Zhang2, Nasser Yousefi3,4
1School of Intelligent Manufacturing, Changchun Sci-Tech University, Changchun, Jilin, 130600, China.
Scientific reports
|December 27, 2024
概括
精确的水和能源消耗预测在深对于资源管理至关重要. 一种新的卷积神经网络 (CNN) 方法与增强的大猩猩部队优化 (EGTO) 在预测城市资源需求方面显示出高准确性.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 全球对水和能源的需求正在上升,特别是在城市地区.
- 由于人口和经济增长,深面临着日益增长的消费.
- 准确的预测对于可持续的资源管理至关重要.
研究的目的:
- 开发一种创新的预测技术,用于深的水和能源消耗.
- 将气候,社会经济和人口因素纳入消费预测.
- 提高城市资源管理的预测准确性和可靠性.
主要方法:
- 使用一个卷积神经网络 (CNN) 模型.
- 将CNN与增强的大猩猩部队优化 (EGTO) 算法集成.
- 与现有方法对模型的性能进行了验证.
主要成果:
- 实现了高相关系数:水为0.87,能源消耗为0.91.
- 已证明的低误差率:水的MAE为0.63,能源的RMSE为0.58.
- 提出的CNN-EGTO方法的表现优于其他领先的预测方法.
结论:
- 开发的技术为城市的水和能源消耗提供了准确的预测.
- 结果支持对资源分配和可持续性的知情决策.
- 纳入气候和社会经济因素对于有效的资源预测至关重要.
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