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.

For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image classification datasets with the help of AutoML models. In particular, the paper studies Bayesian optimization in depth and proposes the use of genetic algorithm, differential evolution and covariance matrix adaptation—evolutionary strategy for acquisition function optimization. Moreover, we compare these variants of Bayesian optimization with conventional Bayesian optimization and observe that the use of covariance matrix adaptation—evolutionary strategy and differential evolution improves the performance of standard Bayesian optimization. We also notice that Bayesian optimization tends to perform poorly when genetic algorithm is used for acquisition function optimization.

Machine learning strategy has changed the face of automated models by integrating themselves into many application domains. The spectrum of applications ranges over various domains, from atmospheric analysis to medical diagnosis. All these applications are design-sensitive, implying that the model's performance depends highly on the selected machine learning algorithm, training procedures, regularization methods, and most importantly, how the hyperparameters are tuned. With the advent of AutoML systems, all these processes including hyperparameter optimization can be automated, producing better performance and faster results. The ideal hyperparameter setting for machine learning models has a direct and significant impact on the model's performance. This paper studies different AutoML models and the hyperparameter techniques used by them for image classification problems. We also discuss some of the libraries for hyperparameter optimization and analyse how they work for different image classification problems. Moreover, the paper proposes a framework for hyperparameter optimization that combines Bayesian optimization and evolutionary algorithms. Experiments are carried out on image classification benchmark datasets to assess the performance of different optimization approaches including the proposed model.