Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques

Dinu Maria Jose, Amala Mary Vincent, Gowdagere Siddaramaiah Dwarakish

Scientific Reports · 18 March 2022

In one sentence. Compares ML ensembling (RF, SVR, ETR, LSTM) of 34 GCMs for daily precipitation and temperature in an Indian basin; LSTM reaches R² of 0.9 for precipitation.

Abstract

Abstract Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by various techniques like arithmetic mean, Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extra Tree Regressor (ETR), Random Forest (RF) and long short-term memory (LSTM). The 21 General Circulation Models (GCMs) from National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and 13 GCMs of Coupled Model Inter-comparison Project, Phase 6 (CMIP6) are used for this purpose. The results of the study reveal that the application of a LSTM model for ensembling performs significantly better than models in the case of precipitation with a coefficient of determination (R 2 ) value of 0.9. In case of temperature, all the machine learning (ML) methods showed equally good performance, with RF and LSTM performing consistently well in all the cases of temperature with R 2 value ranging from 0.82 to 0.93. Hence, based on this study RF and LSTM methods are recommended for creation of MMEs in the basin. In general, all ML approaches performed better than mean ensemble approach.

Labels

Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.