融合改进优化算法与深度学习的海平面变化预测Sea Level ChangePrediction Method Integrating Improved Optimization Algorithm and Deep Learning
朱湘杰,刘娉婷,韩任智,王胜平,陈志高
摘要(Abstract)：
【目的】提升海平面预测精度，提高防范水文灾害的能力。【方法】采用融合切比雪夫混沌映射（Chebyshev）和反向差分学习（OBL-DE）机制改进鹈鹕算法，结合双向长短期记忆网络（Bi-directional long shortterm memory, BILSTM）预测模型，为BILSTM寻找最佳参数组，利用日本验潮站数据，通过使用机器学习方法测定未来海平高变化，计算预测值与实际值的平均绝对值、均方根误差、平均绝对百分比误差。【结果】提出一种改进鹈鹕优化算法（Improve pelican optimization algorithm, IPOA）解决鹈鹕优化算法在实际应用中容易陷入局部最优的问题，基于IPOA-BILSTM模型寻找的最优参数组为[366,450,0.01,488]，提高在复杂环境下探索最优解的效率。8个预测模型的海平面高对比实验结果表明，IPOA-BILSTM预测模型的预测误差最低，平均绝对误差值为10.53 mm、均方根误差值28.69 mm、平均绝对百分比误差0.14%，结果验证了IPOA-BILSTM模型更适合该区域的海平面高预测。【结论】本研究提出的预测模型IPOA-BILSTM，在日本验潮站数据中实现了高性能的预测，提升了海平面预测的精度，为海平面变化研究提供参考。
关键词(KeyWords)： 海平面高;优化算法;深度学习;预测模型
基金项目(Foundation): 自然资源部海洋环境探测技术与应用重点实验室开放基金资助项目（MESTA-2023-A003）;; 国家自然科学基金（42266006）;; 江西省重点研发计划（20212BBE53031）
作者(Author): 朱湘杰,刘娉婷,韩任智,王胜平,陈志高
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