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.
The advancement in
artificial intelligence (AI) 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 insufficiently user friendly.
There are several algorithms and methods which can be used, in this report
these techniques are explore, the techniques are as follows, knowledge based
systems, genetic algorithm, artificial neural network and fuzzy inference
for using AI
Many model users do not
possess the requisite knowledge to glean their input data, build algorithmic
models and evaluate their results. This may produce inferior designs causing
underutilization or total failure of the model. Due a computer uses memory and
speed, a balance between speed and accuracy need to be struck.
This technique 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 an
evolutionary algorithm that uses selection, reproductive, crossover and
Genetic Algorithm uses
computational models of natural evolutionary process in developing computer
based problems solving systems.
This technique 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 technique 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.
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. Some future
directions for further development and their potentials are explored and
presented. It is believed, with the ever-heightening capability of AI
technologies, that further development of numerical modelling in this direction
will be promisi