Big data and deep learning, application in hydrology

Big data and DL could become an indispensable tool in hydrology. Perhaps one day DL will become an inalienable component of the hydrologic discipline itself, just as Bayesian statistics or numerical solutions to differential equations have. Currently, we are still in the early value discovery stage of DL development in hydrologic science.

Artificial Intelligence often refers to machines that can perform any intellectual task that humans can. Such machines are heavily romanticized and are still very far from becoming a reality. However, weak or narrow AIs, algorithms that are designed to perform a specific task, have shown a formidable intellectual prowess that surpasses human capabilities in certain tasks. These machines must have integrative decision-making capability based on what they receive and what they predict would happen.

Moving forward, deep learning has considerable potential to improve process-based models. Process-based models require human pre-conceptions of the physical systems, and they can elucidate causes and effects and bridge data gaps. Deep learning and process-based models clearly complement each other and can have synergies. More novel approaches need to be developed in the context of big yet unbalanced data, complex and trans-disciplinary process-based models, and observational uncertainty, to explore how deep learning can be used to advance mechanistic modeling in the hydrologic sciences.

Underneath their recent success, deep learning DL often serves as a predictive tool by modeling relationships between inputs and their outcomes. DL helps AI anticipate consequences of its actions, learn features and make decisions. Powered by DL, the performance of recent AIs has improved dramatically, and new solutions have emerged across various fields in a Cambrian explosion fashion, including medical diagnosis, natural language processing, and engineering designs.

A significant advantage of DL for science is that it can be a highly-efficient scientific explorer that is relatively unconstrained by human pre-conceptions. Specifically, we can ask what kind of relationship a deep network has found and whether it identified connections that we overlooked. The Go community already refers to AlphaGo as a teacher and requests its assistance in human understanding of the game. Due to its ability to automatically extract features from data, DL offers a fresh and unique opportunity to detect these relationships, interpret their meanings and expand our knowledge.

Source: HOB

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