Introduction an evolutionary algorithm that uses selection, reproductive,

Introduction

Numerical models are
frequently used to simulate the flow and water quality problems. Usually,
selecting a suitable numerical model to solve a practical water quality problem
is a highly specialised task requiring detailed knowledge on the application
and limitation of models. Due to the complexity, there is an increasing demand
to integrate artificial intelligence (AI) with these mathematical models in
order to assist selection and manipulation.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Project
Purpose

The advancement in
artificial intelligence over the past few decades has made it possible to
integrate technologies into numerical modelling systems to remove constraints
produced by current numerical models which are not user friendly. Many users do
not have the adequate knowledge to get their input data for an algorithmic
model and evaluate their results. A problem in modelling is when a parameter is
change in a model, the results may vary. This may lead to inferior designs
causing a failure of the model. There are several algorithms and methods which
can be used, in this report, knowledge based systems, genetic algorithm,
artificial neural network and fuzzy inference system techniques are explored. Each
AI techniques has different algorithms thus has different applications in water
quality testing; each application was assessed to see which AI technique would
fit best for a specific task.

Results

Knowledge
Based Systems is a technique that uses symbolic and logical
reasoning algorithm. Knowledge based systems mimic and automate the decision
making and reasoning processes of human expects in solving problems. This
technique uses a collection of general facts, rules of thumb and casual models
of the behaviour specific to the problem domain. This can be used for the
selection and manipulation of various numerical models on water quality.

Genetic
Algorithm is a technique that uses an evolutionary
algorithm that uses selection, reproductive, crossover and mutation. Genetic
Algorithm uses computational models of natural evolutionary process in
developing computer based problems solving systems. This can be used for the
optimization of calibration of the parameters of numerical models on water
quality.

Artificial
Neural Network is a technique that uses data driven models
approached with highly interconnected processing elements. Artificial Neural
Network uses an information processing paradigm that is inspired by biological
nervous systems in simulating underlying relationships that are not fully
understood. This can be used for the determination of underlying physical/
biological relationships that are not fully understood.

Fuzzy
inference system is a technique that uses map elements of a
fizzy set to a universe of membership values. Fuzzy inference systems use
modelling complex and imprecise systems when objective or the constraints are
vague using a function theoretic membership form belonging to the close
interval from 0 to 1. Quantification of the semantemes of the expertise and
determine the confidence factors of the semantemes.

Future Directions

All the AI technique are able
to carry out specific task in relation to water quality testing, however, a versatile
technique is to combine all these techniques together. It is believed, with the
ever-heightening capability of AI technologies, that further development of
numerical modelling in this direction will be promising. More efficient AI
techniques will arise, addressing the key issues with the current AI techniques
and produce more user-friendly systems with a clearer knowledge representation.

Conclusion

This study has reviewed
the progress on the integration of AI into water quality modelling. The
integration of various AI techniques, including knowledge based systems, genetic
algorithms, artificial neural networks and fuzzy inference system, into
numerical modelling systems have been reviewed where it was found that these
techniques can contribute to the integrated model in different aspects and may
not be mutually exclusive to one another.