Water is an important resource in many of the human activities. Thus, water management should include an integrated view of several distinct systems from different domains: environment, agriculture, industry, etc. As a result, there are many complex interactions between different factors, some of them not immediately apparent. In this context there is an imminent need for complex modelling solutions for water-based systems.

A model is a simplified, schematic representation of the real world. Models are meant to help engineers, scientists and decision–makers to determine what is happening in reality and to predict what may happen in the future. In particular, they are useful for the assessment of the impact of human activities on the environment or on artificial systems.

A classical definition of the model is “a simplification of reality over some time period or spatial extent, intended to promote understanding of the real system” [Bellinger, 2006] or “A model is a simplification of reality that retains enough aspects of the original system to make if useful to the modeller” [Eykhoff, 1974]. In this context the system is defined to be a part of reality (isolated from the rest), which consists of entities that are in mutual relationships (processes) and have limited interactions with the reality outside of the system.

A model is a physical or mathematical description of a physical system including the interaction with the outside surrounding environment, which can be used to simulate the effect of changes in the system itself or the effect of changes due to conditions imposed on the system.

The selection of the appropriate model together with the associated parameters is an important element in modelling water related problems. [K.W. Chau, 2007]

Nowadays modelling solutions are used intensively in hydroinformatics. There are two main paradigms in modelling aquatic environment physically-based modelling and data-driven modelling [Donald K., 2005].

Physically based distributed modelling, uses a description of the physical phenomenon which govern the behaviour of water in the system under study. The principles that are applied are mass conservation and additional laws describing the driving forces.

Results of modelling depends on the level of knowledge being encapsulated within the software package.

Physically based models (see Section 3.1) are considered to be deterministic when they provide a unique output to a given input. The main advantage of such approach is that it can be applied to a wide range of input data after the initial testing and calibration of the model has been carried out. One of the disadvantages of this approach is that they can generate a large amount of information, since they require small steps of computation both in space and time.

Different solutions of modelling software tools (see Section 3.1, Available software tools) led to improvements in the understanding of large-scale water-based systems, such as rivers or coastal waters. Many of these solutions have been extended to include external influence factors such as advection and dispersion of pollutants in the flow, the transport of sediment in suspension or other similar examples.

The second modelling approach is data-driven for which the main principle is to connects one set of output data with the corresponding input set. Such a model can only work if enough observed (measured data) are available. The model is based on finding different correlations between data sets in order to determine the best input-output pair.

There are several data-driven modelling techniques, such as (see Section 3.2): Neural Networks, Nearest neighbour model, Genetic algorithms model, Fuzzy rule based system model, Decision/model tree model, and Support vector machine model.