scholarly journals Using Adjacent Buoy Information to Predict Wave Heights of Typhoons Offshore of Northeastern Taiwan

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1800 ◽  
Author(s):  
Chih-Chiang Wei ◽  
Chia-Jung Hsieh
Keyword(s):  
Prediction Model ◽  
Wave Height ◽  
Prediction Accuracy ◽  
Complete Information ◽  
Rogue Waves ◽  
Superior Performance ◽  
Efficiency Coefficient ◽  
Height Prediction ◽  
Relative Errors ◽  
Sea Area

In the northeastern sea area of Taiwan, typhoon-induced long waves often cause rogue waves that endanger human lives. Therefore, having the ability to predict wave height during the typhoon period is critical. The Central Weather Bureau maintains the Longdong and Guishandao buoys in the northeastern sea area of Taiwan to conduct long-term monitoring and collect oceanographic data. However, records have often become lost and the buoys have suffered other malfunctions, causing a lack of complete information concerning wind-generated waves. The goal of the present study was to determine the feasibility of using information collected from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of data from both buoys are discussed herein. This study established a prediction model, and two scenarios were used to assess the performance: Scenario 1 included information from the adjacent buoy and Scenario 2 did not. An artificial neural network was used to establish the wave height prediction model. The research results demonstrated that (1) Scenario 1 achieved superior performance with respect to absolute errors, relative errors, and efficiency coefficient (CE) compared with Scenario 2; (2) the CE of Longdong (0.802) was higher than that of Guishandao (0.565); and (3) various types of typhoon paths were observed by examining each typhoon. The present study successfully determined the feasibility of using information from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of both buoys were also discussed.

scholarly journals An EMD-PSO-LSSVM hybrid model for significant wave height prediction

2021 ◽  
Author(s):  
Gang Tang ◽  
Haohao Du ◽  
Xiong Hu ◽  
Yide Wang ◽  
Christophe Claramunt ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Wave Height ◽  
Deep Sea ◽  
Significant Wave Height ◽  
Prediction Accuracy ◽  
Learning Ability ◽  
Support Vector ◽  
Significant Wave ◽  
Height Prediction ◽  
Sea Area

Abstract. Accurate and significant wave height prediction with a couple of hours of warning time should offer major safety improvements for coastal and ocean engineering applications. However, significant wave height phenomenon is nonlinear and nonstationary, which makes any prediction simulation a non straightforward task. The aim of the research presented in this paper is to improve predicted significant wave height via a hybrid algorithm. Firstly, empirical mode decomposition (EMD) is used to preprocess the nonlinear data, which are decomposed into several simple signals. Then, least square support vector machine (LSSVM) with nonlinear learning ability is used to predict the significant wave height, and particle swarm optimization (PSO) is implemented to automatically perform the parameter selection in LSSVM modeling. The EMD-PSO-LSSVM model is used to predict the significant wave height for 1, 3 and 6 hours leading times of two stations in the offshore and deep-sea areas of the North Atlantic Ocean. The results show that the EMD-PSO-LSSVM model can remove the lag in the prediction timing of the single prediction models. Furthermore, the prediction accuracy of the EMD-LSSVM model that has not been optimized in the deep-sea area has been greatly improved, an improvement of the prediction accuracy of Coefficient of determination (R2) from 0.991, 0.982 and 0.959 to 0.993, 0.987 and 0.965, respectively, has been observed. The proposed new hybrid model shows good accuracy and provides an effective way to predict the significant wave height for the deep-sea area.

scholarly journals Design of a High Sea Wave Sensor System in Puger Beach

2020 ◽  
Vol 5 (2) ◽  
pp. 13
Author(s):  
Ike Fibriani ◽  
Januar Fery Irawan ◽  
Alfredo Bayu Satriya ◽  
Satrio Budi Utomo ◽  
Widyono Hadi ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wave Height ◽  
Weather Prediction ◽  
Weather Condition ◽  
Average Error ◽  
Height Measurement ◽  
Height Prediction ◽  
Neural Network Algorithm ◽  
Anemometer System ◽  
Sea Area

Indonesia is an archipelagic country that has a very wide sea area. Thus, Indonesian sea has a huge potential of natural resources that can be utilized to grow the nation's economy. There are many occupations and efforts that can be done to increase the income from the sea and also to conserve it. Fishery is one of the most effective way to gain the sea resources; however, fishery is limited by the weather condition on the sea. This is also a problem that happened in Puger Beach. Puger Beach is located in the south Jember and it faces the Hindia Ocean, which means the weather condition is more dangerous for fishermen than other part of coastal. To ensure the safety of the fishermen, the weather condition on the sea must be evaluated and predicted before the fishery. This study designed a system to provide fishermen in Puger Beach an information about sea and beach weather condition which consist of wave height prediction, wind speed, temperature, humidity and weather prediction. The wind speed is obtained from self-designed anemometer system, the temperature is measured using LM35 sensor, and the humidity is assessed using DHT22. The wave height in the sea was predicted by calculating the wind speed value and effective average fetch value using neural network algorithm. The weather on the sea and on the beach were predicted by rain and light sensor. This weather prediction would be classified into three different results, namely raining, cloudy and bright. After some experiments, the result showed that the device can provide the information needed for fishermen and it has a high sensing accuracy. The humidity measurement had an average error of 1.1%, the temperature measurement had 1.42% average error, and 2.37% for the wind speed measurement. The wave height measurement system worked out and found the average wave height in Puger Beach 0.37 meters.

An Improved Multivariate Weather Prediction Model Using Deep Neural Networks and Particle Swarm Optimisation

2021 ◽  
pp. 2150029
Author(s):  
K U Jaseena ◽  
Binsu C Kovoor
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Weather Prediction ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Dew Point ◽  
Percentage Error ◽  
Support Vector ◽  
Efficiency Coefficient ◽  
Proposed Model

Accurate weather prediction is always a challenge for meteorologists. This paper suggests a Deep Neural Network (DNN) model to predict minimum and maximum values of temperature based on various weather parameters such as humidity, dew point, and wind speed. Particle Swarm Optimisation (PSO) algorithm is applied to select relevant and important features of the datasets to improve the prediction accuracy of the model. The grid search algorithm is employed to determine the hyperparameters of the proposed DNN model. The statistical indicators Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, Nash–Sutcliffe model efficiency coefficient, and Correlation Coefficient are used to evaluate the accuracy of the prediction model. Performance comparison of the proposed model is performed with the Support Vector Machine (SVM) and Vector Autoregression (VAR) models. The experimental outcomes show that the proposed model optimised using PSO achieves better prediction accuracy than traditional approaches.

Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction

2011 ◽  
Vol 38 (2-3) ◽  
pp. 487-497 ◽  
Author(s):  
Iman Malekmohamadi ◽  
Mohammad Reza Bazargan-Lari ◽  
Reza Kerachian ◽  
Mohammad Reza Nikoo ◽  
Mahsa Fallahnia
Keyword(s):  
Wave Height ◽  
Height Prediction

Genetic Programming based Drag Model with Improved Prediction Accuracy for Fluidization Systems

2017 ◽  
Vol 15 (2) ◽  
Author(s):  
R. R. Sonolikar ◽  
M. P. Patil ◽  
R. B. Mankar ◽  
S. S. Tambe ◽  
B. D. Kulkarni
Keyword(s):  
Pressure Drop ◽  
Genetic Programming ◽  
Drag Coefficient ◽  
Prediction Accuracy ◽  
Volume Fraction ◽  
Solid Volume Fraction ◽  
Superior Performance ◽  
Momentum Exchange ◽  
Drag Model ◽  
Parameter Ranges

Abstract The drag coefficient plays a vital role in the modeling of gas-solid flows. Its knowledge is essential for understanding the momentum exchange between the gas and solid phases of a fluidization system, and correctly predicting the related hydrodynamics. There exists a number of models for predicting the magnitude of the drag coefficient. However, their major limitation is that they predict widely differing drag coefficient values over same parameter ranges. The parameter ranges over which models possess a good drag prediction accuracy are also not specified explicitly. Accordingly, the present investigation employs Geldart’s group B particles fluidization data from various studies covering wide ranges of Re and εs to propose a new unified drag coefficient model. A novel artificial intelligence based formalism namely genetic programming (GP) has been used to obtain this model. It is developed using the pressure drop approach, and its performance has been assessed rigorously for predicting the bed height, pressure drop, and solid volume fraction at different magnitudes of Reynolds number, by simulating a 3D bubbling fluidized bed. The new drag model has been found to possess better prediction accuracy and applicability over a much wider range of Re and εs than a number of existing models. Owing to the superior performance of the new drag model, it has a potential to gainfully replace the existing drag models in predicting the hydrodynamic behavior of fluidized beds.

scholarly journals Wave Height Distributions and Rogue Waves in the Eastern Mediterranean

2021 ◽  
Vol 9 (6) ◽  
pp. 660
Author(s):  
Sagi Knobler ◽  
Daniel Bar ◽  
Rotem Cohen ◽  
Dan Liberzon
Keyword(s):  
Wave Height ◽  
Eastern Mediterranean ◽  
Rogue Waves ◽  
Distribution Model ◽  
Storm Events ◽  
Order Model ◽  
Deep Seawater ◽  
Wave Heights ◽  
Storm Characteristics ◽  
Buoy Data

There is a lack of scientific knowledge about the physical sea characteristics of the eastern part of the Mediterranean Sea. The current work offers a comprehensive view of wave fields in southern Israel waters covering a period between January 2017 and June 2018. The analyzed data were collected by a meteorological buoy providing wind and waves parameters. As expected for this area, the strongest storm events occurred throughout October–April. In this paper, we analyze the buoy data following two main objectives—identifying the most appropriate statistical distribution model and examining wave data in search of rogue wave presence. The objectives were accomplished by comparing a number of models suitable for deep seawater waves. The Tayfun—Fedele 3rd order model showed the best agreement with the tail of the empirical wave heights distribution. Examination of different statistical thresholds for the identification of rogue waves resulted in the detection of 99 unique waves, all of relatively low height, except for one wave that reached 12.2 m in height which was detected during a powerful January 2018 storm. Characteristics of the detected rogue waves were examined, revealing the majority of them presenting crest to trough symmetry. This finding calls for a reevaluation of the crest amplitude being equal to or above 1.25 the significant wave height threshold which assumes rogue waves carry most of their energy in the crest.

Enhanced Hybrid Model to Predict the Surface Roughness of Honed cylinder Bore

2021 ◽  
pp. 1-25
Author(s):  
Burhan Afzal ◽  
Xueping Zhang ◽  
Anil Srivastava
Keyword(s):  
Surface Roughness ◽  
Hybrid Model ◽  
Surface Texture ◽  
Prediction Accuracy ◽  
Analytical Models ◽  
Superior Performance ◽  
Functional Surface ◽  
Model Framework ◽  
Multiple Variables ◽  
Cylinder Bore

Abstract Cylinder bore honing is a finishing process that generates a crosshatch pattern with alternate valleys and plateaus responsible for enhancing lubrication and preventing gas and oil leakage in the engine cylinder bore. The required functional surface in the cylinder bore is generated by a sequential honing process and is characterized by Rk roughness parameters (Rk, Rvk, Rpk, Mr1, Mr2). Predicting the desired surface roughness relies primarily on two techniques: (i) analytical models (AM) and (ii) machine learning (ML) models. Both of these techniques offer certain advantages and limitations. AM's are interpretable as they indicate distinct mapping relation between input variables and honed surface texture. However, AM's are usually based on simplified assumptions to ensure the traceability of multiple variables. Consequently, their prediction accuracy is adversely impacted when these assumptions are not satisfied. However, ML models accurately predict the surface texture but their prediction mechanism is challenging to interpret. Furthermore, the ML models' performance relies heavily on the representativeness of data employed in developing them. Thus, either prediction accuracy or model interpretability suffers when AM and ML models are implemented independently. This study proposes a hybrid model framework to incorporate the benefits of AM and ML simultaneously. In the hybrid model, an Artificial neural network (ANN) compensates the AM by correcting its error. This retains the physical understanding built into the model while simultaneously enhancing the prediction accuracy. The proposed approach resulted in a hybrid model that significantly improved the prediction accuracy of the AM and additionally provided superior performance compared to independent ANN.

scholarly journals Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Hao Long ◽  
Chaofeng Liang ◽  
Xi’an Zhang ◽  
Luxiong Fang ◽  
Gang Wang ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Prediction Model ◽  
Basic Science ◽  
Prediction Accuracy ◽  
Genetic Basis ◽  
Bioinformatics Analysis ◽  
Clinical Treatment ◽  
Structural Level ◽  
Biotechnological Application ◽  
Key Genes

Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM). The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma. Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy. These findings might provide more insights into the genetic basis of glioblastoma.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

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