Bayesian Networks and Network Science Applied to Water Resources Streamflow Analysis and Forecast

Bayesian Networks and Network Science Applied to Water Resources: Streamflow Analysis and Forecast

Presented at the 10th Annual BayesiaLab Conference on Monday, October 24, 2022.


The thesis entitled “Bayesian Networks and Network Science Applied to Water Resources: Streamflow Analysis and Forecast incorporating the Non-Stationarity” aimed to develop methodologies to (1) identify the existence of changes in the streamflow time series and its location, (2) incorporate this aspect in the streamflow modeling and forecasting framework and (3) analyze the full extension regarding its impact. The focus on Bayesian Networks, as an alternative approach to classical streamflow modeling methodologies, relied upon recent articles that indicated Bayesian Networks as a promising tool in hydroclimate studies, simultaneously providing good modeling results and allowing causal discovery through the analysis of the network structure. A first attempt to incorporate this non-stationarity was made using Gaussian Bayesian Networks (GBN). Discrete variables representing the different phases of low-frequency oscillations were included in the networks, allowing different network parameters according to the phases. The results demonstrated the great potential of the GBN to forecast streamflow with lead times from one to eight months. The results also unveiled a good streamflow forecasting potential via Bayesian Inference based on Likelihood Weighting simulations. The use of the phases resulted in performance improvement for some stations, however, it did not improve the results of the stations that presented changes in the time series, suggesting significant changes between the network structures of each homogeneous period. Network structures were obtained through different methodologies for each homogeneous period to analyze this aspect. The results confirmed the initial hypothesis, showing significant differences between the network structures of each homogeneous period, with alterations in the relationship between the variables and their autocorrelation function. Therefore, the use of the same set of parents for the complete series may not comprise the extension of the changes observed.

Presentation Video

Presentation Slides

About the Presenters

Dr. Renan Rocha is a Civil Engineer with a Master's and Doctor degree in Water Resources from the Federal University of Ceará (UFC). Currently works as a Researcher in FUNCEME, Institute for Research in Meteorology, Water Resources, and Environment, and is the Head of the Water Resources Department (GEPEH). Has experience with Time Series Analysis, Hydrological Modelling, Bayesian Networks, Complex Networks, Drought analysis, and NEXUS (Water-Energy-Food). His recent thesis explored the use of Bayesian Networks to forecast streamflow incorporating the Non-Stationarity existence.

Dr. Francisco de Assis de Souza Filho is a professor at the Hydraulics and Environmental Engineering Department of the Federal University of Ceará and the Head Scientist of Water Resources at the Ceará State Foundation for the Support of Scientific and Technological Development. Has a Doctor degree from São Paulo University and a postdoctoral internship at the International Research Institute for Climate and Society of Columbia University. Has won several awards, including the Engineer Francisco Gonçalves Aguiar Medal, the highest commendation of the Water Resources of Ceará. Was the head of water resources-related organizations, such as FUNCEME and ABRH.

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