Funding body: Engineering and Physical Sciences Research Council (EPSRC)

The objective identified in the proposal was the investigation of potential applications for genetic programming (GP) that would be of benefit to the water industry. The proposal also stressed that the project would develop a novel algorithm and that the investigation would not simply consist of applying the existing genetic programming (Koza, 1992; 1994) algorithm to selected problems. The importance of comparing the new method with existing techniques was also identified. In recent years many methods for creating "black box" mathematical models have been reported in the engineering literature. The methods include artificial neural networks, polynomial networks and genetic programming.

The project deals with the use of data mining techniques on the Royal Mail risk database. A sample database was supplied on which encouraging results were found. The data mining techniques employed here each attempt to find patterns and trends in a database with greater accuracy than standard statistical techniques. These are designed to find relationships between seemingly unrelated sets of data. The data in the risk database consists of several attributes of a Post Office and the number of incidents it has suffered in the past three years. The task of the data mining tool is to find what (if any) reasons there are behind an office being more prone to incident than another. Each technique had different errors on the Royal Mail database.

Funding body: ERASMUS scheme (European Community)

This project presents an application of Neural Networks (NNs) to rainfall-runoff modelling. Applications of the neural network technique in this domain of hydrology have so far provided accurate results for small storm events on theoretical catchments (Minns & Hall, 1995). The aim of the research presented in this report was to investigate the application of NNs, as 'black-box' models of rainfall-runoff processes, on real catchments. The NN approach is tested and compared to optimised conceptual hydrological models applied to a catchment over a period of several years. At the same time, all tests and experiments were done in parallel with a Genetic Programming technique (Cousin, 1997).

Funding body: ERASMUS scheme (European Community)

Data driven modelling techniques have gained in popularity in the last 20 years. They are more cost effective compared to the development of mechanistic models. Furthermore, those mechanistic models are highly non-linear and complex, which makes them difficult to identify and use. Currently, the majority of data driven modelling methods can be categorised under two headings: artificial neural networks and statistical and regression analysis. Neural networks can usually provide models that are capable of good predictions, but they don't give any insight into the structure of the process. They are commonly called black boxes, one puts the data in and gets results from the model, but does not know anything about the underlying relationships between input and output data. It is usually desirable to gain some insight into the underlying process structures, as well as make accurate numeric predictions. The aim of this work is to develop a computer software, that uses genetic operations in order to find a symbolic equation describing the relationship between input and output data.