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

Manali Pal; Rajib Maity; J V Ratnam; Masami Nonaka; Swadhin K Behera

doi:10.1038/s41598-019-57183-3

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

Sci Rep. 2020 Jan 15;10(1):365. doi: 10.1038/s41598-019-57183-3.

Authors

Manali Pal¹, Rajib Maity², J V Ratnam³, Masami Nonaka³, Swadhin K Behera³

Affiliations

¹ Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
² Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India. rajib@civil.iitkgp.ac.in.
³ Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.

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.