Dataset Information

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

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 (R²) 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 R² 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.

SUBMITTER: Jose DM

PROVIDER: S-EPMC8933560 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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

Jose Dinu Maria DM Vincent Amala Mary AM Dwarakish Gowdagere Siddaramaiah GS

Scientific reports 20220318 1

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 S ...[more]

PMID: 35304552

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Dataset Information

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

Publications

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

Similar Datasets

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