Unknown

Dataset Information

0

Long-lead Prediction of ENSO Modoki Index using Machine Learning algorithms.


ABSTRACT: 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 anomalies of the slowly varying climate variables such as sea surface temperature (SST), sea surface height (SSH) and soil moisture content (SMC). The importance of each of the predictors is evaluated using the Supervised Principal Component Analysis (SPCA). The results indicate both SVR and RF to be capable of forecasting the phase of the EMI realistically at both 6-months and 12-months lead times though the amplitude of the EMI is underestimated for the strong events. The analysis also indicates the SVR to perform better than the RF method in forecasting the EMI.

SUBMITTER: Pal M 

PROVIDER: S-EPMC6962158 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

altmetric image

Publications

Long-lead Prediction of ENSO Modoki Index using Machine Learning algorithms.

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]

Similar Datasets

| S-EPMC5831789 | biostudies-literature
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
| S-EPMC4914443 | biostudies-literature
| S-EPMC8049120 | biostudies-literature
| S-EPMC9060714 | biostudies-literature
| S-EPMC10432552 | biostudies-literature
2013-01-01 | GSE29210 | GEO
| S-EPMC8044535 | biostudies-literature
| S-EPMC9085875 | biostudies-literature
| S-EPMC6390190 | biostudies-other