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<p>Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Niño Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggested&#xa0;study concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictors&#xa0;as mentioned above.</p>

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Climate predictors in Indian summer monsoon forecasting: a novel De-correlated RVFL ensemble strategy

  • 期刊论文

  • 发表日期：

    2025年1月2日

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  • Yajnaseni Dash,
  • Ajith Abraham,
  • Naween Kumar,
  • Dileep Kumar Yadav,
  • Neha Singhal

摘要

Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Niño Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggested study concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictors as mentioned above.