1.2 Machine learning methods
ML methods are part of artificial intelligence (AI) which is a broad term for tools that mimic cognitive human capabilities. The use of AI has rapidly increased in recent years. The number of peer-reviewed publications across all fields between 2000 and 2019 has grown around 12 times (D. Zhang et al., 2021) and with them, multiple algorithms, architectures, and tools have been created. Fields in which ML methods have shown outstanding results include computer vision, speech recognition, and language processing. Most of these applications use supervised learning, which identifies a branch of ML that is similar to RS metamodelling. Supervised ML employs a set of input-output examples, also known as the labelled training dataset, to calibrate a model by minimizing the error between the model predictions and the values assumed as ground truth. This set of algorithms usually increase their performance at a given task as the amount of labelled examples grows larger. Due to their successes, supervised ML methods, and in particular deep learning (DL) and artificial neural networks (ANNs), are widely employed for surrogate modelling across many fields of science and engineering (Liu et al., 2021; Peng et al., 2020; Wu et al., 2020). Although scientific studies on ML applications for water resources date back to over two decades ago (Maier & Dandy, 2000), Hadjimichael et al. (2016) noted that this trend is not necessarily witnessed in the urban water sector.