Forecasting the event prior to its actual occurrence
is an attempt to prepare for the inevitable. To
this end, Hydrology, whose doctrine could be dated
back to as early as the pre-historic era, based on for
example, natural observations and more often than
not, with religious beliefs affiliation. Nonetheless, its
more modernized research has evolved into scientifically
constructing a model of hydrological processes,
with the goal to offer timely and accurate estimates
of future discharge at specific watershed locations. A
wide variety of forecasting techniques avail-able typically
adopted rainfall-runoff modeling (Fig 1) and
many models have been proposed with differing modifications.
Thus far, there remain deficits: these models
were unable to generalize well and thus barely
overcome higher-than-average accuracy limit, thanks
to intransigent over other spatial causative factors.
More recent years, there have been many studies on
flood susceptibility and flood prediction using Geographical
Information System (GIS). They commonly
rely on analyzing relevant predicting factors, associated
with weights and rates specified by human experts,
normally are employed from variety of fields.
Furthermore, collecting these information is not only
time consuming but also impractical and inappropriate
in many cases. Land slope, for instance, is a
prime factor, as it retains high significance in flood
predictability for Angthong province [3]. This same
factor however, is not eligible for those areas on river
basin such as Pathumthani province, whose slopes fall
within as narrow as 0-5% range [2].