Model induction from data: towards the next generation of computational engines in hydraulics and hydrology; Proefschrift Technische Universiteit Delft.

There has been an explosive growth of methods in recent years for learning(or estimatingdependency) from data, where data refers to the known samples that are combinations of inputs and corresponding outputs of a given physical system. The main subject that is addressed in this thesisis, therefore, model induction from data for the simulation of hydrodynamic processes in the aquatic environment. First, some currently popular artificial neural network architectures are introduced, and it is then argued that these devices can be regarded as domain knowledge in capsulators by applying the method to the generation of wave equations fro m hydraulic data and showing how the equations of numerical-hydraulic models can, in their turn, be recaptured using artificial neural networks.The thesis also demonstrates how artificial neural networks can be used to generate numerical operators on non-structured grids for the simulation of hydrodynamic processes in two-dimensional flow systems and a methodology has been derived for developing generic hydrodynamic models using artificial neural network.The thesis also highlights one other model induction technique, namely that of the support vector machine, as an emerging new method with a potential to provide more robust models

Modelling the Effects of Historical and Future Land Cover Changes on the Hydrology of an Amazonian Basin

Land cover changes (LCC) affect the water balance (WB), changing surface runoff (SurfQ), evapotranspiration (ET), groundwater (GW) regimes, and streamflow (Q). The Tapajós Basin (southeastern Amazon) has experienced LCC over the last 40 years, with increasing LCC rates projected for the near future. Several studies have addressed the effects of climate changes on the region’s hydrology, but few have explored the effects of LCC on its hydrological regime. In this study, the Soil and Water Assessment Tool (SWAT) was applied to model the LCC effects on the hydrology of the Upper Crepori River Basin (medium Tapajós Basin), using historical and projected LCC based on conservation policies (GOV_2050) and on the “Business as Usual” trend (BAU_2050). LCC that occurred from 1973 to 2012, increased Q by 2.5%, without noticeably altering the average annual WB. The future GOV_2050 and BAU_2050 scenarios increased SurfQ by 238.87% and 300.90%, and Q by 2.53% and 2.97%, respectively, and reduced GW by 4.00% and 5.21%, and ET by 2.07% and 2.43%, respectively. Results suggest that the increase in deforestation will intensify floods and low-flow events, and that the conservation policies considered in the GOV_2050 scenario may still compromise the region’s hydrology at a comparable level to that of the BAU_2050