Medium‐Term Forecasting of Loop Current Eddy Cameron and Eddy Darwin Formation in the Gulf of Mexico With a Divide‐and‐Conquer Machine Learning Approach

2019 ◽  
Vol 124 (8) ◽  
pp. 5586-5606
Author(s):  
Justin L. Wang ◽  
Hanqi Zhuang ◽  
Laurent M. Chérubin ◽  
Ali K. Ibrahim ◽  
Ali Muhamed Ali
Keyword(s):  
Machine Learning ◽  
Gulf Of Mexico ◽  
Divide And Conquer ◽  
Learning Approach ◽  
Loop Current ◽  
Medium Term ◽  
Machine Learning Approach

scholarly journals Loop Current SSH Forecasting: A New Domain Partitioning Approach for a Machine Learning Model

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 570-579
Author(s):  
Justin L. Wang ◽  
Hanqi Zhuang ◽  
Laurent Chérubin ◽  
Ali Muhamed Ali ◽  
Ali Ibrahim
Keyword(s):  
Machine Learning ◽  
Current System ◽  
Model Performance ◽  
Region Of Interest ◽  
Divide And Conquer ◽  
Loop Current ◽  
Event Prediction ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Domain Partitioning

A divide-and-conquer (DAC) machine learning approach was first proposed by Wang et al. to forecast the sea surface height (SSH) of the Loop Current System (LCS) in the Gulf of Mexico. In this DAC approach, the forecast domain was divided into non-overlapping partitions, each of which had their own prediction model. The full domain SSH prediction was recovered by interpolating the SSH across each partition boundaries. Although the original DAC model was able to predict the LCS evolution and eddy shedding more than two months and three months in advance, respectively, growing errors at the partition boundaries negatively affected the model forecasting skills. In the study herein, a new partitioning method, which consists of overlapping partitions is presented. The region of interest is divided into 50%-overlapping partitions. At each prediction step, the SSH value at each point is computed from overlapping partitions, which significantly reduces the occurrence of unrealistic SSH features at partition boundaries. This new approach led to a significant improvement of the overall model performance both in terms of features prediction such as the location of the LC eddy SSH contours but also in terms of event prediction, such as the LC ring separation. We observed an approximate 12% decrease in error over a 10-week prediction, and also show that this method can approximate the location and shedding of eddy Cameron better than the original DAC method.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document