School of Science, Engineering and Information Technology

Developing artificial intelligence techniques for streamflow prediction and comparison with other predictive models

Project Title:

Developing artificial intelligence techniques for streamflow prediction and comparison with other predictive models

Supervisor(s):

Associate Professor Andrew Barton

Dr. Tanveer Choudhury

Dr. Harpreet Kandra

Contact person and email address:

Associate Professor Andrew Barton

a.barton@federation.edu.au

A brief description of the project:

Streams and rivers play a critical role in the hydrologic cycle with their management being essential to maintaining a balance across social, economic and environmental outcomes. Accurate streamflow predictions, especially in the changing climatic conditions, can provide benefits in many different ways such as water allocation decision making, flood forecasting and environmental watering regimes. This is particularly important in the context of regional areas of Australia and developing countries where rivers can play a critical role in irrigated agriculture, recreation and social wellbeing, major floods and sustainable environments. There are several hydrological parameters that affect flows in rivers and water bodies and a major challenge with any prediction methodology, is to understand these parameter interdependencies, correlations and their individual effects. A robust methodology is, thus, required for accurate prediction of streamflow under usually unique, waterway-specific conditions using available data. This research proposes to develop artificial intelligence techniques to develop robust predictive methodology for this purpose and developing an information management system that facilitates collection of data needed for robust modelling. It also aims to carry out a sensitivity analysis and undertake comparisons with existing methodologies to validate and insert confidence in the proposed technique.