基于机器学习的某河道路堤沉降预测

Prediction of Settlement of a Certain River Embankment Based on Machine Learning

本研究旨在探讨机器学习技术在河道路堤沉降预测中的应用,特别是采用长短期记忆神经网络(LSTM)对河道路堤的沉降行为进行建模和预测。系统地收集河道路堤的历史沉降监测数据,随后对数据进行严格的预处理,以消除异常值和噪声,确保数据质量。在此基础上,构建基于LSTM的沉降预测模型,并利用优化算法对模型进行训练和参数调优。通过交叉验证和性能评估指标,对模型的预测能力进行验证。结果显示,LSTM模型在预测河道路堤沉降方面表现出了较高的准确性,能够有效地捕捉沉降数据的时序特征,并具有较强的泛化能力。研究旨在为河道路堤的工程设计、施工和维护提供依据。

This study aims to explore the application of machine learning technology in predicting the settlement of river embankments,especially by using long short-term memory neural networks(LSTM)to model and predict the settlement behavior of river embankments.The historical settlement monitoring data of river embankments are systematically collected and then the data to eliminate outliers and noise are rigorously preprocessed to ensure data quality.On this basis,a settlement prediction model based on LSTM is constructed,and optimization algorithms are used to train and optimize the model parameters.The predictive ability of the model is validated through cross validation and performance evaluation metrics.The results show that the LSTM model exhibits high accuracy in predicting the settlement of river embankments,effectively capturing the temporal characteristics of settlement data and has strong generalization ability.The research aims to provide a basis for the engineering design,construction,and maintenance of river embankments.