The International Environmetrics Society (TIES) has launched a new
TIES Webinar Series on Data Science for Environmental Sciences (DSES).
Our next webinar will be on
October 27, at
11 am Central Time (attached flyer).
You can virtually access the webinar and register via our website:
www.environmetrics.xyzSpeaker: Rendani Mbuvha, Queen Mary University of London.
Title: Leveraging global streamflow prediction systems for imputation of missing in-situ observations in West Africa
Abstract: Streamflow observation data is vital for flood monitoring, agricultural, and settlement planning. However, such streamflow data are commonly plagued with missing observations due to various causes such as harsh environmental conditions and constrained operational resources. This problem is often more pervasive in under-resourced areas such as Sub-Saharan Africa. We reconstruct streamflow time series data through bias correction of the GEOGloWS ECMWF streamflow service (GESS) forecasts at ten river gauging stations in Benin Republic. We perform bias correction by fitting Quantile Mapping, Gaussian Process, and Elastic Net regression in a constrained training period. We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in low predictive skill over the ten Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior skill relative to traditional imputation by Random Forest, k-Nearest Neighbour, and GESS lookup. The findings of this work provide a basis for integrating global GESS streamflow data into operational early-warning decision-making systems (e.g., flood alert) in countries vulnerable to drought and flooding due to extreme weather events.
Bio: Rendani Mbuvha is the DeepMind academic fellow in Machine learning at the Queen Mary University of London and an Associate Professor in Actuarial Science at the University of Witwatersrand, South Africa. He is a Fellow of the Actuarial Society of South Africa and a holder of the Chartered Enterprise Risk Actuary designation. Rendani's research focuses on Probabilistic Machine Learning and its applications in climate science and financial risk management.
Hope to see you all there!
Ignacio Segovia-Dominguez,
On behalf of the TIES Webinar Series' organizing committee
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Ignacio Segovia-Dominguez
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