Flow and Sediment Prediction at Ungauged Basins Using Artificial Intelligence Models and Entropy Index

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Atieh, Maya

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University of Guelph


The prediction of streamflow and sediment load statistics at locations within ungauged remote basins remains one of the most uncertain modelling tasks in hydrology. The intent of this research was to gain a better understanding of flow and sediment load statistics at ungauged basins through 1) developing artificial neural networks (ANN), and gene expression programming (GEP) models that address the complex nonlinear effect of physio-climatic parameters on flow duration curve (FDC) and sediment rating curve (SRC) statistics, 2) determining the most important physio-climatic parameters impacting FDC parameters (mean, variance), and SRC parameters (rating coefficient and exponent), 3) introducing an entropy parameter, apportionment entropy disorder index (AEDI), that represents precipitation variability, 4) adopting techniques within ANN models to cope with data scarcity including the Dropout method and synthetic minority over-sampling technique (SMOTE), and 5) assessing the impacts of flow regulation on FDC parameters. ANN models trained and tested on 147 stations in Ontario, Canada, revealed that climatic, topographic and land cover characteristics were the most important inputs defining average flow. Topographic and hydrologic characteristics were the most important parameters defining flow variability. ANN and GEP models trained and tested on 260 regulated and unregulated gauging stations across North America showed that drainage area followed by mean annual precipitation, shape factor and AEDI were the most influential parameters on average flow. Regulation was found to affect flow variability and had no significant impact on average flow. Dropout and SMOTE techniques improved model performance. ANN models trained and tested on 94 gauged streams in Ontario, Canada revealed that the rating coefficient is positively correlated to rainfall erosivity factor, soil erodibility factor, and AEDI and negatively correlated to vegetation cover and mean annual snowfall. The rating exponent was found to be positively correlated to mean annual precipitation, AEDI, main channel slope, standard deviation of flow and negatively correlated to the fraction of basin area covered by water. AEDI has been successfully integrated in the FDC and SRC prediction models. Including AEDI parameter in FDC and SRC models improved model performance. This thesis recommends using AEDI in future hydrological modelling research.



Flow Duration Curve, Sediment Rating Curve, Ungauged Basins, Entropy, Artificial Neural Networks