Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling

Highlights

• DL is rooted in connectionism, hyper-flexibility, and vigorous optimization, which are alien to process-based modelling.

• A knowledge base is essential to enable predictions of complex, open, partially observable, and non-stationary systems.

• Bridging DL and process-based modelling is embryonic but has great potential in an age of big data and computational power.

Abstract

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new ‘data-driven’ technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from ‘process-based’, mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions that might be posed by earth and environmental scientists, and with the aid of a real-world modelling experiment, it explains some critical, but often ignored, issues DL may face in practice. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where AI models can (1) embrace the typically ignored knowledge base available, (2) function credibly in ‘true’ out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field.