Predicting rainfall response to climate change and uncertainty analysis: introducing a novel downscaling CMIP6 models technique based on the stacking ensemble machine learning

Abstract

Abstract This study proposes a novel downscaling technique based on stacking ensemble machine learning (SEML) to predict rainfall under climate change. The SEML consists of two levels. Rainfall time series predicted by level 1 algorithms MLR, MNLR, MARS, M5, RF, LSBoost, LSSVM-GS, and a novel hybrid algorithm namely LSSVM-RUN) are used as inputs to the level 2 machine learning algorithm (MARS and LSSVM_RUN). Then, meta-algorithms of SEML predict rainfall based on eight predicted rainfall in level 1. This approach boosts prediction accuracy by utilizing the strong points of different machine learning (ML) algorithms. Results showed that MARS and LSSVM-RUN could be employed to improve the modeling results as meta-algorithms (level 2 of the SEML). Three global climate models (GCMs) in the historical period (1985–2014) and three SSP scenarios in the future period (2021–2050) were considered for downscaling and predicting rainfall at Lake Urmia and Sefidrood basins. Using meta-algorithms, the prediction results showed that rainfall in all scenarios and stations decreased between 0.02 and 0.20% (except Takab station in model CanESM5 scenarios). Hence, the proposed stacking ensemble ML has the potential for modeling and predicting precipitation with good accuracy and high reliability.