STAHY 2023 – 13th International Workshop on Statistical Hydrology (STAHY)

The International Commission on Statistical Hydrology (ICSH) of the International Association of Hydrological Sciences (IAHS) invites researchers to submit abstracts for presentation at the 13th International Workshop on Statistical Hydrology (STAHY2023), which will be hosted by Northeastern University in Boston, Massaschuetts (USA), from 8-10 November 2023.

The STAHY 2023 workshop brings together the international statistical hydrology community for vibrant scientific discussions and debates on advanced statistical methods for hydrological applications. This year’s theme aims to provide a bridge between the environmental statistics and artificial intelligence communities with methodological discussions, exchange of knowledge, and identification of opportunities for mutual support to solve climate, water, and sustainability issues.

Focusing on the broader scope of the Sustainable Development Goals (SDGs) established by the United Nations in 2015, the theme of the workshop is expected to address several goals but are not limited to Clean Water and Sanitation (SDG 6), Sustainable Cities and Communities (SDG 11), Climate Action (SDG 13), and Life on Land (SDG 15).

Artificial intelligence and machine learning are creating a renaissance for environmental and hydrologic statistics that STAHY 2023 wishes to capture in its theme. For example, machine learning, including the latest generative pre-trained transformers (GPTs), cannot work without probability theory, and similarly nonlinear statistics can benefit from the flexibility offered by neural network processing. Integrating AI technologies with environmental and hydrologic statistics would provide valuable insights for policymakers to devise informed planning and robust water resources management decisions. Similarly, in this era of Big Data and automated decisions, a wide range of statistical theories such as extreme value theory and human-in-the-loop decisions, continue to remain relevant.

We welcome contributions that use machine learning, artificial intelligence, hydrologic statistics and/or environmental statistics and address climate, water, and sustainability issues. Sessions will be arranged by scientific topic and aim to include the diverse ways in which traditional environmental statistics, artificial intelligence, and machine learning are applied within these topics.