Chapter 9 - Averaging multiclimate model prediction of streamflow in the machine learning paradigm

Abstract

Climate change continues to change the course of streamflow regimes. River basins around the world are experiencing frequent floods and/or droughts due to climate change. To carry out robust water management, climate models are increasingly being used to simulate the low and high flow regimes. However, due to inherent parametric differences among the models, climate models often produce different predictions. The model selection has been used to select the best performing climate model. Besides model selection, model averaging has also been adopted in order to take advantage of the relative strengths of the respective climate models. The climate models’ streamflow is averaged in different paradigms such as Bayesian frameworks and machine learning paradigms. The objective of this chapter is to synthesize the recent application of the machine learning paradigm in averaging climate model–based streamflow simulation. The machine learning paradigms considered include the neural networks and the support vector regression models.