Ontology highlight
ABSTRACT:
SUBMITTER: Pal M
PROVIDER: S-EPMC6962158 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
Pal Manali M Maity Rajib R Ratnam J V JV Nonaka Masami M Behera Swadhin K SK
Scientific reports 20200115 1
The focus of this study is to evaluate the efficacy of Machine Learning (ML) algorithms in the long-lead prediction of El Niño (La Niña) Modoki (ENSO Modoki) index (EMI). We evaluated two widely used non-linear ML algorithms namely Support Vector Regression (SVR) and Random Forest (RF) to forecast the EMI at various lead times, viz. 6, 12, 18 and 24 months. The predictors for the EMI are identified using Kendall's tau correlation coefficient between the monthly EMI index and the monthly anomalie ...[more]