The Effect of Data Skewness on the LSTM-Based Mooring Load Prediction Model

Abstract

The working condition of the floating platform will be affected by wind and waves in the marine environment. Therefore, it is of great importance to carry out real-time prediction research on the mooring load for ensuring the normal operation of the floating platform. Current researches have focused on the real-time prediction of mooring load using the machine learning method, but most of the studies are about the application and generalization analysis of different models. There are few studies on the influence of data distribution characteristics on prediction accuracy. In view of the above problems, this paper investigates the effect of data skewness on the prediction performance for the deep learning model. The long short-term memory (LSTM) neural network is applied to construct the mooring load prediction model. The numerical simulation datasets of the deep water semi-submersible platform are employed in model training and data analysis. The prediction performance of the model is preliminarily verified based on the simulation results. Meanwhile, the distribution characteristics of mooring load data under different sea states are analyzed and a skewness processing method based on the Box-Cox Transformation (BCT) is proposed. The effect of data skewness on prediction accuracy is further investigated. The comparison results indicate that reducing the mooring load data skewness can effectively improve the prediction accuracy of LSTM model.

The Propagation Velocity and Influences of Environmental Factors of Deterministic Sea Wave Prediction in the Long Crest Wave

“…To further analyze the prediction effect, this paper calculated the Root Mean Square Error (RMSE) for the time history segments of Figure 1a,b, respectively. RMSE is one of the most commonly used errors to measure the degree of fit between time series and the formula is shown in Equation ( 17) [39].…”

Prediction of Specific Fuel Consumption of a Tractor during the Tillage Process Using an Artificial Neural Network Method

“…However, the node-shoot dataset showed a high peak kurtosis and positively high skewness, indicating a higher likelihood of outlier values in this dataset than in others. The normal distribution, which is symmetrical and has zero skewness, is compared to the skewed distribution [52]. Negative skewness demonstrates that extra data are dispersed on the left side of the data mean, while positive skewness discloses that extra data are scattered on the right side of the mean.…”

“…Negative skewness demonstrates that extra data are dispersed on the left side of the data mean, while positive skewness discloses that extra data are scattered on the right side of the mean. As shown in Tables 1 and 2, the values of skewness reflect the mark of data asymmetry to be positive for the specific fuel consumptions for chisel and moldboard plows of 0.03 and 1.71, respectively; however, there was an impact of data skewness on prediction accuracy, as most machine learning techniques frequently accept that variables follow a normal distribution [52]. In this study, the kurtosis value was in the range of −0.39 to 4.06 for chisel plow parameters and in the range of −0.52 to 5.24 for moldboard plow parameters, as shown in Tables 1 and 2, respectively.…”

A Deep Learning-Based Approach to Failure Detection in Mooring (Thin) Lines from Marine Images

“…Furthermore, the deep learning-based LSTM model was proposed by [11,12] for mooring line load monitoring to perform offshore mooring operations safely. They proposed a Box-Cox transformation (BCT) model to improve the predicting accuracy.…”