SWAT and ANN model hydrological assessment using Malaysia soil data / Khairi Khalid

Flood is a primary hazard affecting Malaysia, often responsible for loss
of lives and a severe threat to infrastructure and environment. Activities
in flood plain and catchment such as land clearing for other developments
effort may increase the magnitude of a flood. The problem of flood
management cannot be solved simply by providing more construction of
dams and reservoirs. The adoption of a strategic approach is needed for
planning and managing that flood management in any watershed. The
evolution of distributed watershed models has been established for more
accurate representation of the hydrological system by considering the
spatial variability of model parameters and inputs. The research aims to
evaluate the performance and hydrological response of the Soil Water
Assessment Tool (SWAT) process-based model in tropical river basin
using Malaysia soil data. This study was specific to the upper part of
Langat River Basin (UPLRB) in the context of Greater Kuala Lumpur Plan
in the southern region. The research also provides a streamflow prediction
using the Artificial Neural Network (ANN) method as another tool for
assessment. ArcSWAT2009.93.b, which is embedded in ArcGIS10, has
been selected for this study, and the model requires comprehensive data
on topography, soils, land use and daily weather data within a watershed.
SWAT-CUP, which links SUFI-2 algorithm to SWAT models, has been
utilized in the study for the calibration of SWAT models.