Exploding the myths: An introduction to artificial neural networks for prediction and forecasting

作者：Holger R. Maier, Stefano Galelli, Saman Razavi, Andrea Castelletti, Andrea Emilio Rizzoli, Ioannis N. Athanasiadis, Miquel Sànchez–Marrè, Marco Acutis, Wenyan Wu, Greer B. Humphrey · 发表于：Environmental Modelling & Software · 年份：2023 · DOI：10.1016/j.envsoft.2023.105776 · 被引用次数：140 · 研究领域：Hydrological Forecasting Using AI、Energy Load and Power Forecasting、Stock Market Forecasting Methods

Artificial Neural Networks (ANNs), sometimes also called models for deep learning, are used extensively for the prediction of a range of environmental variables. While the potential of ANNs is unquestioned, they are surrounded by an air of mystery and intrigue, leading to a lack of understanding of their inner workings. This has led to the perpetuation of a number of myths, resulting in the misconception that applying ANNs primarily involves “throwing” a large amount of data at “black-box” software packages. While this is a convenient way to side-step the principles applied to the development of other types of models, this comes at significant cost in terms of the usefulness of the resulting models. To address these issues, this inroductory overview paper explodes a number of the common myths surrounding the use of ANNs and outlines state-of-the-art approaches to developing ANNs that enable them to be applied with confidence in practice.