Application of deep learning in streamflow prediction
Project Title:
Application of deep learning in streamflow prediction
Supervisor(s):
Dr. Tanveer Choudhury
Associate Professor Andrew Barton
Dr. Harpreet Kandra
Dr. Sunil Aryal
Contact person and email address:
Dr. Tanveer Choudhury
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 can provide benefits in many different ways such as water allocation decision making, flood forecasting and environmental watering regimes. There are several hydrological parameters that affect stream flows in rivers 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 study of deep learning to develop robust predictive methodology for this purpose. It also aims to carry out a sensitivity analysis and develop a comparative study with existing methodologies to validate and insert confidence in the proposed methodology.