An ensemble machine learning framework for streamflow data reconstruction

This project will develop a new framework to reconstruct missing streamflow data using advanced machine learning techniques. Reliable streamflow records are essential for flood forecasting, drought monitoring, and water resource planning, but many stations have missing or incomplete data. The proposed approach will combine traditional statistical methods with modern single-learner and ensemble machine learning models to estimate missing values more accurately across North American river basins. The collaboration between the University of Saskatchewan and UNAM will strengthen expertise in statistical hydrology and deliver practical tools that contribute to efforts toward improving water management and water security for both countries.

Faculty Supervisor:

Cuauhtemoc Tonatiuh Vidrio Sahagun

Student:

Partner:

Universidad Nacional Autónoma de México

Discipline:

Engineering

Sector:

Education

University:

University of Saskatchewan

Program: