Model linking by mimicking hydrological and hydraulic models with artificial neural networks

In the daily practice of water management it is often necessary to couple simulation models. Hydrological engineers model the water cycle involving precipitation, runoff, rivers, lakes, groundwater and tidal estuaries. Their solutions have to be simultaneously sustainable, safe and economically feasible. To meet these requirements, it is necessary to find solutions with multiple functions. Engineers regularly use computers for the simulation of hydrological systems. Model coupling is necessary to connect simulation models from different disciplines. Model coupling is defined as the coupling of separate simulation models to form an integrated system in which calculation results are shared. In an ideal environment a researcher or engineer can investigate the effects of a certain design using a single tool. However, in reality such integrated design tools are not always available. The researcher or engineer has to find budget, time and technology to solve this problem. In most cases he/she has limited resources and as a result is restricted in his/her choice of applications and simulation models. The objective of this thesis is to investigate an alternative method for model coupling using artificial neural networks (ANNs). ANNs are networks of simple processing units called neurons and are conceived as elementary models of the human brain. The type of ANNs mostly used is the multilayer perceptron network. This is a feed-forward network trained with a back propagation learning process. ANNs can be used to model dynamic and complex relations between observed inputs and outputs of a physical system. History and time delays can be implemented by a delay line, with recurrent connections or with moving averages. ANNs have been used to emulate hydrological models. The ANNs can, after training, emulate the hydrological models without the software package in which the hydrological models have been built. The next step is to couple the ANNs by connecting the input and output variables of the models. The advantage is that the coupling of the original software packages is not necessary. Links between different software packages do exist, however these links are not always compatible to any software program. Building connections between software packages require costly changes to the source code. In this thesis ANNs are investigated and applied to the coupling of hydrodynamic, hydrological and water quality models. The hydrological models are emulated by ANNs and coupled by connecting the ANNs which makes the coupling independent of the original software packages. The approach of this research is both technical and practical. A method is proposed in which ANNs can simulate model results. The design steps to train a neural network are data selection, neural network design and network optimisation and training. A number of the examples from literature show it is possible to simulate hydrological systems with ANNs without process knowledge. A Rainfall-Runoff model is a good example. There are also examples of small, hydrological systems for elementary river reaches or hydraulic systems with limited number of input/output points in space. Experiments are performed to test the accuracy of ANNs in simulating hydrological models. The first tests focus on basic river reaches. The design and training of ANNs for hydraulic river reaches have been tested under various conditions. The starting point of the experiments is a one-dimensional river reach. It has a prismatic channel with rectangular cross-sections. It is similar to an elementary river reach. Such model has a clear and traceable relation between input water level and downstream water level output. With a standard, feed-forward ANN it is possible to create accurate results for steady state situations. An ANN for the same model under conditions of gradually varying dynamic flow also gives good predictions. Under rapidly changing conditions the ANN produces less accurate results. For such dynamic