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基于深度神经网络的预测,由红树林森林减弱海浪潮

Didit Adytia¹, Dede Tarwidi^1,2, Deni Saepudin¹

¹School of Computing, Telkom University, Jalan Telekomunikasi No. 1 Terusan Buah Batu, Bandung 40257, Indonesia.

|July 8, 2024

概括

深度神经网络 (DNN) 准确地预测红树林如何减少海波的影响. 这种先进的模型为沿海保护战略的传统方法提供了可靠的替代方案.

科学领域:

沿海工程沿海工程
环境科学环境科学
计算流体动力学的流体动力学.

背景情况:

红树林森林充当了对抗海浪潮的自然屏障.
预测红树林的浪潮减弱能力对于沿海防御至关重要.
现有的模型在模拟复杂的波浪时可能缺乏准确性或效率. 互动. 互动. 互动. 互动. 互动.

研究的目的:

开发一个深度神经网络 (DNN) 模型来预测由红树林森林造成的海浪潮减弱.
评估DNN模型的准确性和性能.
将DNN模型性能与其他机器学习算法进行比较.

主要方法:

模拟的海浪潮减弱使用Boussinesq模型与分层网格近似.
在实验室实验中验证了Boussinesq模型 (MAE 0.003-0.01).
使用超过4万个模拟数据点训练了一个DNN模型,优化了超参数和架构.

主要成果:

DNN模型实现了高的确定系数 (R2=0.99560).
预测误差很低:MAE=0.00118,RMSE=0.00151,MAPE=3%. 预测误差很小,因为MAE=0.00118,RMSE=0.00151,MAPE=3%.
DNN的性能优于支持向量回归 (SVR) 和多重线性回归 (MLR),相当于极端梯度增强 (XGBoost).

结论:

优化的DNN模型为红树林介导的海波减弱提供了高度准确的预测.
DNN模型显示出作为实证公式和经典数值模型的替代品的巨大潜力.
开发的DNN模型显示,预测林造成的海浪潮减弱的误差不到3%.

关键词:

亚当优化器亚当优化器 DNN用于波衰减预测的DNN.深度神经网络是一个神经网络.超参数优化超参数优化这是一场海和海.波的减弱是波的减弱.

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