Stream Flowrate Prediction Using Genetic Programming
Model in a Semi-Arid Coastal Watershed
World Water and Environmental Resources Congress
2005, Anchorage, Alaska, USA, (May 2005)DOI: doi:10.1061/40792(173)352
Abstract
Effective water resources management is a critically
important priority across the globe. The availability
of adequate fresh water is a fundamental requirement
for the sustainability of human and terrestrial
landscapes, and the importance of understanding and
improving predictive capacity regarding all aspects of
the global and regional water cycle is certain to
continue to increase. One fundamental component of the
water cycle is stream discharge. Stream flowrate
prediction is not only related to regular water supply
for human, animal, and plant populations, but also
relevant for the management of natural hazards, such as
drought and flood, that occur abruptly resulting in
economic loss. Efforts to improve existing methods and
develop new methods of stream flow prediction would
support the optimal management of water resources at
all scales in space and time. Recent advances in
genetic programming technologies have shown potential
to improve the prediction accuracy of stream flow rate
in some river systems by better capturing the
non-linearity of the features embedded in a system.
This study elicits microclimatological factors in
association with the basin-wide geological environment,
exhibits the derivation of a representative genetic
programming model, summarises the non-linear behaviour
between the rainfall/run-off patterns, and conducts
stream flow rate prediction in a river system given the
influence of dynamic basin features such as soil
moisture, soil texture, vegetative cover, air
temperature, and precipitation rate. Three weather
stations are deployed as a supplementary data-gathering
network in addition to over 10 existing gage stations
in the semi-arid Nueces River Basin, South Texas. An
integrated database of physical basin features is
developed and used to support a semi-structure genetic
programming modelling approach to perform stream
flowrate predictions. The genetic programming model is
eventually proved useful in forecasting stream flowrate
in the study area where water resources scarce issues
are deemed critical.