强大的适应性优化,用于可持续的水需求预测在水分系统
Ke Wang1,2, Jiayang Meng3, Zhangquan Wang1
1College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, 310015, China.
Scientific reports
|February 3, 2025
概括
本研究介绍了一种强大的自适应优化分解 (RAOD) 策略,用于准确预测水需求. 这种新的方法提高了可持续水资源管理的预测准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水资源管理 水资源管理
背景情况:
- 物联网的进步需要智能水需求预测,以实现可持续性.
- 水消耗数据的非静态性挑战了现有模型的预测准确性.
研究的目的:
- 为准确的水需求预测引入一种新的强大的自适应优化分解 (RAOD) 策略.
- 为应对水消耗数据非静止性和非线性所带来的挑战.
主要方法:
- 在 RAOD 策略中,使用完整集体实证模式分解 (CEEMD) 来进行数据预处理.
- 一个优化算法最大限度地减少了多尺度排列的变异,以提高概括性.
- 深度神经网络用于使用分解数据精确预测水需求.
主要成果:
- 与现有模型相比,RAOD模型在所有指标上都表现出卓越的性能.
- 来自四个地区的现实世界数据集验证了该模型的多步预测准确性.
- 该策略有效地减轻了水需求系列中的非静止性和非线性.
结论:
- RAOD策略为预测水需求提供了一个强大而准确的解决方案.
- 这种方法适合在可持续的水资源管理中可靠地预测水需求.
- 该模型的增强的概括能力确保它适合于各种地理应用.
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