Rainfall–runoff modelling is at the core of any hydrological forecasting system. The high spatio-temporal variability precipitation patterns, complexity physical processes, and large quantity parameters required to characterize a watershed make prediction runoff rates quite difficult. In this study, hyper-complex artificial neural network in form an octonion-valued (OVNN) proposed estimate rate...

Rainfall-Runoff models are mostly empirical in nature demanding the knowledge of a large number of catchment parameters. On the contrary Artificial Neural Networks (ANN) can be deployed in cases where the available data is limited. The present work involves the development of an ANN model using Backward Propagation algorithm. The hydrologic variables used were Rainfall, Soil Moisture, Evaporati...

The stormwater runoff and infiltration performance of pavement structures has been systematically evaluated within an extensive research project. Over 230 field and lab scale experiments have been completed and analyzed for surface runoff and infiltration characteristics as well as for water content changes inside the construction. The test series comprises several types of conventional and exp...

The major problem in the assessment of relationships between rainfall and runoff occurs when a study is carried out in ungauged watersheds in the absence of hydro-climatic data. This study aims to evaluate the applicability of Natural Resources Conservation Service-Curve Number (NRCS-CN) method together with GIS in estimating runoff depth in a mountainous watershed. The study was carried out in...

Ice jams cause major flooding and severe damages to communities and infrastructure along the Saint John River. There is a growing need to develop capability in forecasting and analyzing icejam-related flood events. This capability is also essential in anticipating the potential for increased ice jam damages as a result of a changing climate. The HEC-RAS model, which can simulate ice jam configu...