Dam Settlement Prediction Based on Random Error Extraction and Multi-Input LSTM Network

Abstract

The prediction of dam settlement data plays an important role in analyzing whether the dam is in a safe operation state. But in the field of surveying engineering, the original data measured by instruments will inevitably have random and unpredictable random errors, and the settlement of dams usually has a strong correlation with environmental parameters. In this study, the influence of random error and environmental parameters on dam settlement prediction is discussed, and a prediction model based on multi-input long short-term memory (LSTM) network and random error extraction is proposed. Through the settlement data of a concrete face rockfill dam, the analysis shows that removing random errors can significantly improve the short-term prediction performance and considering environmental parameters can significantly improve the long-term prediction performance. In addition, through comparison and generalization experiments, this method not only has higher prediction accuracy, but also can be applied to other surveying and mapping engineering fields.