Code and data for "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction" By Guan et al. Submitted to JGR Oceans.

This repository contains the code and data for the machine learning analysis of "Seasonal Surface Eddy Mixing in the Kuroshio Extension: Estimation and Machine Learning Prediction”By Guan et al. Submitted to JGR Oceans.

Specifically, this repository contains the following items:
(1) Codes for assessing the representation skill of the machine learning and linear regression (LR) methods. Three machine learning methods are considered: random forest (RF), back-propagation neural network (BP), and convolutional neural network (CNN).
(2) Codes for assessing the prediction skill of the machine learning and LR methods.  
(3) Seasonal-mean and annual-mean input data to run these codes.  
(4) The package needed to run the random forest code, i.e. the RF_MexStandalone-v0.02 program package from https://code.google.com/archive/p/randomforest-matlab/downloads .