一种基于CEEMD和改进的LSTM的联合月度降水预测方法
1College of Information and Intelligence, Hunan Agricultural University, Changsha Hunan, PR China.
PloS one
|July 13, 2023
概括
准确的降水预测对于水资源管理至关重要. 一种新方法结合了互补集体实证模式分解 (CEEMD) 与修改的长期短期记忆 (LSTM) 神经网络,以改进每月降雨预测.
科学领域:
- 水文学的水文学
- 气候科学 气候科学
- 数据科学数据科学数据科学
背景情况:
- 减少的水资源需要准确的降水预测,以有效地管理水资源.
- 月度降水数据表现出复杂的非线性和非静止特征,对传统预测模型构成挑战.
研究的目的:
- 开发一种新的月度降水综合预测方法.
- 为了应对降水数据中非线性和非静态性的挑战.
- 提高降水预报的准确性和可靠性.
主要方法:
- 使用互补集体实证模式分解 (CEEMD) 将月度降水序列分解为静止子序列.
- 使用修改的长短期记忆 (LSTM) 神经网络预测单个子序列.
- 通过粒子优化 (PSO) 算法优化LSTM超参数.
- 预测子序列的叠加以获得最终的降水预报.
主要成果:
- CEEMD-LSTM 方法成功分解了降水数据,揭示了当地特征和非线性动态.
- 粒子集群优化有效优化了LSTM超参数,减少了预测随机性.
- 与传统方法相比,CEEMD-LSTM组合模型在预测月度降水方面表现优越.
- 该模型准确地捕获了降水趋势,并在一个案例研究中显示出更高的预测准确性.
结论:
- 拟议的CEEMD-LSTM方法为每月降水预测提供了一个强大的方法,有效地处理复杂的数据特征.
- 这种先进的预测技术为合理的水资源分配和管理提供了宝贵的见解.
- 该研究强调了混合分解机器学习模型在环境预测中的潜力.
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