scholarly journals A Glider Simulation Model Based on Optimized Support Vector Regression for Efficient Coordinated Observation

2021 ◽  
Vol 8 ◽  
Author(s):  
Fangjie Yu ◽  
Zhiyuan Zhuang ◽  
Jie Yang ◽  
Ge Chen
Keyword(s):  
Simulation Model ◽  
Support Vector Regression ◽  
Current Data ◽  
Ocean Current ◽  
Support Vector ◽  
Information Feedback ◽  
Stable Performance ◽  
Prohibitive Cost ◽  
In Suit ◽  
Rapid Modeling

Multi-gliders have been widely deployed as an array in nowadays ocean observation for fine and long-term ocean research, especially in deep-sea exploration. However, the strong, variable ocean currents and the delayed information feedback of gliders are remaining huge challenges for the deployment of glider arrays which may cause that the observed data cannot meet the study needs and bring a prohibitive cost. In this paper, we develop a Glider Simulation Model (GSM) based on the support vector regression with the particle swarm optimization (PSO)-SVR algorithm to integrate the information feedback from gliders and ocean current data for rapid modeling to effectively predict the gliders’ trajectories. Based on the real-time predictive information of the trajectories, each glider can select future movement strategies. We utilize the in-suit datasets obtained by sea-wing gliders in ocean observation to train and test the simulation model. The results show that GSM has an effective and stable performance. The information obtained from the modeling approaches can be utilized for the optimization of the deployment of the glider arrays.

Robust Life-Cycle Production Optimization With a Support-Vector-Regression Proxy

SPE Journal ◽  
2018 ◽  
Vol 23 (06) ◽  
pp. 2409-2427 ◽  
Author(s):  
Zhenyu Guo ◽  
Albert C. Reynolds
Keyword(s):  
Life Cycle ◽  
Simulation Model ◽  
Support Vector Regression ◽  
Reservoir Simulation ◽  
Production Optimization ◽  
Forward Model ◽  
Support Vector ◽  
Well Control ◽  
Steepest Ascent ◽  
Reservoir Simulation Model

Summary We design a new and general work flow for efficient estimation of the optimal well controls for the robust production-optimization problem using support-vector regression (SVR), where the cost function is the net present value (NPV). Given a set of simulation results, an SVR model is built as a proxy to approximate a reservoir-simulation model, and then the estimated optimal controls are found by maximizing NPV using the SVR proxy as the forward model. The gradient of the SVR model can be computed analytically so the steepest-ascent algorithm can easily and efficiently be applied to maximize NPV. Then, the well-control optimization is performed using an SVR model as the forward model with a steepest-ascent algorithm. To the best of our knowledge, this is the first SVR application to the optimal well-control problem. We provide insight and information on proper training of the SVR proxy for life-cycle production optimization. In particular, we develop and implement a new iterative-sampling-refinement algorithm that is designed specifically to promote the accuracy of the SVR model for robust production optimization. One key observation that is important for reservoir optimization is that SVR produces a high-fidelity model near an optimal point, but at points far away, we only need SVR to produce reasonable approximations of the predicting output from the reservoir-simulation model. Because running an SVR model is computationally more efficient than running a full-scale reservoir-simulation model, the large computational cost spent on multiple forward-reservoir-simulation runs for robust optimization is significantly reduced by applying the proposed method. We compare the performance of the proposed method using the SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir-simulations runs for three synthetic examples, including one field-scale example. We also compare the optimization performance of our proposed method with that obtained from a linear-response-surface model and multiple SVR proxies that are built for each of the geological models.

scholarly journals Visualising and predicting the COVID-19 outbreak in Malaysia using network analysis and support vector regression

2021 ◽  
Author(s):  
Siti Nurhidayah Sharin ◽  
Mohamad Khairil Radzali ◽  
Muhamad Shirwan Abdullah Sani
Keyword(s):  
Network Analysis ◽  
Support Vector Regression ◽  
Mean Squared Error ◽  
Current Data ◽  
Support Vector ◽  
Cumulative Number ◽  
Mean Square ◽  
Squared Error ◽  
The World ◽  
Death Cases

Abstract Coronavirus disease 19 (COVID-19) was first discovered in December 2019 in Wuhan, China and spread quickly throughout the world, affecting the economy, social disruption, and public health. Concerning confirmed and death COVID-19 cases in Malaysia, the correlation of states and prediction model using support vector regression (SVR) associated with COVID-19 in Malaysia are yet to discover. Hence, the proposed works employ network analysis and SVR from July 2020 (Q3 2020) until June 2021 (Q2 2021) based on given data by the Ministry of Health Malaysia (MoH) (i) to correlate and visualise the COVID-19 pandemic spread between the states and (ii) to predict the cumulative number of COVID-19 confirmed and death cases. Network analysis was employed using Spearman rank coefficients and revealed an increasing degree of connectedness between different states, thus pinpointing key actors of transmission. Meanwhile, the proposed SVR predictive model could forecast the future COVID-19 cases and deaths (July 2021 to December 2021), with an excellent regression score (R2 = 0.829) and low mean squared error (MSE = 0.171), as well as root mean square error (RMSE = 0.413); hence, making this model reliable enough. Current data demonstrate that network analysis and the SVR model provide insightful and potential information to minimise COVID-19 transmission.

Knowledge Based ε-Support Vector Regression Method

2013 ◽  
Vol 462-463 ◽  
pp. 472-475
Author(s):  
Ying Zhong Shi ◽  
Min Xu ◽  
Pei Lin Liu ◽  
Ping Li
Keyword(s):  
Support Vector Regression ◽  
Partial Information ◽  
Current Model ◽  
Current Data ◽  
Support Vector ◽  
Historical Knowledge ◽  
Learning Abilities ◽  
Regression Methods ◽  
Modeling Methods ◽  
Knowledge Based

The classical regression systems modeling methods only consider the single scene, which has the weakness: partial information missing may weaken the generalization abilities of the regression systems constructed based on this dataset. A regression system with the Knowledge transfer learning abilities, i.e. Knowledge Based ε-Support Vector Regression (KB-ε-SVR for brevity) is proposed based on ε-support vector regression. KB-ε-SVR can use the current data information sufficiently, and learn from the existing useful historical knowledge effectively, so that remedy the information lack in the current scene. Reinforced current model is obtained through control the similarity between current model and history model in the object function and current model can benefit from history scene when information is missing or insufficient. Experiments show that KB-ε-SVR has the better performance and adaptability than the traditional ε-support vector regression methods in scenarios with insufficient data.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

Sign in / Sign up

Export Citation Format

Share Document