1.0 to provide an adequate simulation, this poses a

1.0 Executive summary

1.1 Introduction

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Over
the years, several numerical models have been applied to simulate flow and
water problems in different coastal environments. However, these models are inadequately
user-friendly and as a result lack knowledge transfers in model interpretation.
The primary aim of this paper will be to review the development as well as the
current progress of AI being integrated into water quality modelling.

1.2 Process and planning stage

Initially,
the need of AI integration was assessed in terms of current issues, reasons and
tendency. Currently, models require the use of model manipulation, which aims
to provide an adequate simulation, this poses a problem as it was found out
that many modellers do not posses this skill at an acceptable standard. AI techniques
can carry out the manipulations which gave sufficient reasoning to integrate
the AI. After looking at the tendencies, it could be seen that current modelling
techniques do not incorporate few features to facilitate other users and
methods. Identifying these parameters in the planning stage provided a set of
criteria which would need to be satisfied by the AI in the experimental stage.

1.3 Assessing the AI techniques

Following
the identification of the criteria, several different AI techniques were assessed.
All AI techniques employed different algorithms and the different applications in
water quality testing were assessed to see which method would fit best for a
specific task. The four different AI methods employed were Knowledge-based
systems (KBS), Genetic algorithms (GA), Artificial neural networks (ANN) and
Fuzzy inference systems.

1.4 Results

The
table below shows the results that were obtained after the assessments were
completed. The table indicates which AI technique is best fitted for a specific
application.  

Technique

Water quality applications

Knowledge-based systems (KBS)

Selection and manipulation of various numerical
models on water quality.

Genetic Algorithms (GA)

Optimization of calibration of the parameters of
numerical models on water quality.

Artificial Neural Networks (ANN)

Determination of underlying physical/ biological
relationships that are not fully understood.

Fuzzy inference systems

Quantification of the semantemes of the expertise
and determine the confidence factors of the semantemes.

Table 1- Different applications for
each AI technique

1.5 Future Directions

While
all assessed AI methods are capable of carrying out specific tasks in relation
to water quality testing, a more versatile approach would be to combine these
methods together. Demand for more improved and more efficient AI techniques
will likely rise and as a result research is being carried out to address the
key issues with current AI systems. These new tools will be developed to have
more user-friendly systems as well as clearer knowledge representation.

1.6 Conclusion

The
current techniques used in water quality testing result in many constraints,
and as a result those with little experience in the field will have difficulty
selecting a suitable model. As such, improvements in AI technologies will
minimisie the gap between a developer and practitioner. The assessment of the various
AI techniques into modelling has demonstrated that they can contribute in
various aspects. Furthermore, it is possible that two or more of these
techniques can be combined to produce a more versatile modelling system allowing
for a simpler experience for those new to the practice. Finally, research in
this field will certainly result in better AI systems which would enhance the applications
of this AI in water quality modelling.