Long-term prediction method for the green water load and volume for an assessment of the load line

2003 ◽  
Vol 7 (3) ◽  
pp. 137-144 ◽  
Author(s):  
Yoshitaka Ogawa
Keyword(s):  
Prediction Method ◽  
Green Water ◽  
Water Load ◽  
Load Line ◽  
Term Prediction ◽  
Long Term Prediction

Second Order Model for Wave Crests Used in Prediction of Green Water Load and Volume on Ships in Random Waves

2005 ◽  
Vol 128 (4) ◽  
pp. 271-275 ◽  
Author(s):  
Hanne Therese Wist ◽  
Dag Myrhaug ◽  
Håvard Rue
Keyword(s):  
Prediction Method ◽  
Second Order ◽  
Green Water ◽  
Wave Crest ◽  
Water Load ◽  
Load Line ◽  
Long Term Prediction ◽  
Height Model ◽  
Crest Height

The probability that a wave crest in a random sea will exceed a specified height has long been recognized as important statistics in practical work, e.g., in predicting green water load and volume on a ship. Nonlinear probability density functions for predicting green water load and volume are presented. The models are based on the parametric model of Ogawa (2003, “Long-Term Prediction Method for the Green Water Load and Volume for an Assessment of the Load Line,” J. Marine Sci. Technol., 7, pp. 137–144) combined with transformation of a second order wave crest height model. The wave crest height model is obtained from second order wave theory for a narrow-banded sea state in combination with transformation of the Rayleigh distribution. Results from the second order models are compared with model tests of a cargo ship presented in Ogawa (2003, “Long-Term Prediction Method for the Green Water Load and Volume for an Assessment of the Load Line,” J. Marine Sci. Technol., 7, pp. 137–144) and the Ogawa models.

A Super-Long-Term Prediction Method of Earth Polar Motion Based on Spectrum Analysis

2021 ◽  
pp. 619-628
Author(s):  
Weitao Lu ◽  
Lue Chen ◽  
Zhijin Zhou ◽  
Songtao Han ◽  
Tianpeng Ren
Keyword(s):  
Spectrum Analysis ◽  
Polar Motion ◽  
Prediction Method ◽  
Term Prediction ◽  
Long Term Prediction

Effects of Avoidance of Heavy Weather on the Wave Induced Load on Ships

2006 ◽  
Author(s):  
Zhi Shu ◽  
Torgeir Moan
Keyword(s):  
Green Water ◽  
Vertical Acceleration ◽  
Sea State ◽  
Hull Girder ◽  
Term Prediction ◽  
Strip Theory ◽  
Operational Criteria ◽  
Wave Induced ◽  
Long Term Prediction

This paper is concerned with evaluating the effect of avoidance of heavy weather on the long term wave induced loads on ships. Two hydrodynamic codes VERES based on a 2D strip theory and WASIM based on a 3D Rankine panel method are employed to calculate the wave induced loads and motions on various vessels. Two models for heavy weather avoidance are proposed. The first is based upon the assumption that operational criteria relevant to vertical acceleration, green water and bottom slamming are fulfilled. The second one is based upon the assumption that the sea state forecasts are available to the ship master, and that rerouting is made. And based on the first model considering avoidance of heavy weather and the hydrodynamics results calculated from two codes, the wave induced hull girder loads are obtained. The results are discussed. In particular, the effect of different hydrodynamic codes and various scatter diagrams are assessed. After all, the long term prediction of wave induced hull girder loads considering the effect of avoidance of heavy weather will give a relatively more realistic evaluation of the extreme hull girder loads. Finally the results from ship rules will also be re-evaluated compared with the long term prediction with and without heavy weather avoidance.

scholarly journals A Long-Term Water Quality Prediction Method Based on the Temporal Convolutional Network in Smart Mariculture

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2907
Author(s):  
Yuexin Fu ◽  
Zhuhua Hu ◽  
Yaochi Zhao ◽  
Mengxing Huang
Keyword(s):  
Water Quality ◽  
Prediction Method ◽  
Quality Parameters ◽  
Quality Data ◽  
Convolutional Network ◽  
Water Quality Data ◽  
Water Quality Prediction ◽  
Term Prediction ◽  
Long Term Prediction

In smart mariculture, traditional methods are not only difficult to adapt to the complex, dynamic and changeable environment in open waters, but also have many problems, such as poor accuracy, high time complexity and poor long-term prediction. To solve these deficiencies, a new water quality prediction method based on TCN (temporal convolutional network) is proposed to predict dissolved oxygen, water temperature, and pH. The TCN prediction network can extract time series features and in-depth data features by introducing dilated causal convolution, and has a good effect of long-term prediction. At the same time, it is predicted that the network can process time series data in parallel, which greatly improves the time throughput of the model. Firstly, we arrange the 23,000 sets of water quality data collected in the cages according to time. Secondly, we use the Pearson correlation coefficient method to analyze the correlation information between water quality parameters. Finally, a long-term prediction model of water quality parameters based on a time domain convolutional network is constructed by using prior information and pre-processed water quality data. Experimental results show that long-term prediction method based on TCN has higher accuracy and less time complexity, compared with RNN (recurrent neural network), SRU (simple recurrent unit), BI-SRU (bi-directional simple recurrent unit), GRU (gated recurrent unit) and LSTM (long short-term memory). The prediction accuracy can reach up to 91.91%. The time costs of training model and prediction are reduced by an average of 64.92% and 7.24%, respectively.

scholarly journals Long-term prediction method of reactive load based on LSTM neural network

2020 ◽  
Vol 467 ◽  
pp. 012041
Author(s):  
Xingwei Liu ◽  
Shixiong Fan ◽  
Jiaqi Qin ◽  
Yan Liu ◽  
Wei Wang
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Term Prediction ◽  
Long Term Prediction

Fusion-based Satellite Clock Bias Prediction Considering Characteristics and Fitted Residue

2018 ◽  
Vol 71 (4) ◽  
pp. 955-970 ◽  
Author(s):  
Jicang Lu ◽  
Chao Zhang ◽  
Yong Zheng ◽  
Ruopu Wang
Keyword(s):  
Prediction Models ◽  
Prediction Method ◽  
Satellite System ◽  
Satellite Clock ◽  
Term Prediction ◽  
Mode Decomposition ◽  
Satellite Clock Bias ◽  
Global Navigation Satellite ◽  
Long Term Prediction

As Satellite Clock Bias (SCB) prediction may be affected by various factors such as periodic items, sampling length, and stochastic items, a fusion-based prediction method is proposed by considering characteristics of SCB and fitted residue. On this basis, an instance algorithm is presented by fusing four typical prediction models. First, we use Empirical Mode Decomposition (EMD) to pre-process and decompose the SCB series into multiple components with various characteristics. Then, we analyse the fitting performance of each model for different components and prediction length, namely short-, mid- and long-term prediction, and select models with the best performance. Next, we analyse fitted residue of the reconstructed SCB, and select the model with the best fitting results. Finally, we fuse the multiple selected models for SCB prediction. The method is tested using Global Positioning System (GPS) precise clock products provided by the International Global Navigation Satellite System Service (IGS). Experimental results show that, compared with single prediction models and existing combination models, the proposed fusion-based prediction method improves accuracy and stability. In particular, the proposed method is more stable and has better performance for mid- and long-term prediction.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

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