Development of decision support ststems (dss) models and their applications in groundwater resources exploration and management in Batang Padang area of Perak
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Date
2014
Authors
Kehinde, Mmogaji (Anthony )
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Abstract
The creation of groundwater hydrological database populated with multiple tasks
decision making models to help authority dealing with groundwater exploitation
efficiently is a novel approach in the field of groundwater research domains. This
research involves modeling of both surface and subsurface data sets via application of
Dempster–Shafer theory (DST) of evidential belief function (EBF) model, Ordered
weighted averaging (OWA)-DRASTIC model and Multiple linear regression (MLR)
recharge model to develop decision support system (DSS) models for the purpose of
ensuring effective planning and management of groundwater resources in Batang
Padang area, Perak state, Malaysia. Results from the DST model application produced
two groundwater potential prediction models with accuracies of 75 % and 86 % based
on surface and subsurface parameters, respectively. The surface parameter – based
groundwater potential prediction model map established about 50 % better accuracy
more than the existing hydrogeological map of the area. The results of the application of
the developed predictive borehole yield model resulting from the relationship of the Bel
index values estimated from DST modeling and the area borehole yield data established
the dependency of the area aquifer expected productivity on geology. Furthermore, the
OWA-DRASTIC modeling results produced groundwater vulnerability prediction model
(GVPM) map with 89 % accuracy. The OWA-DRASTIC model-based GVPM accuracy
result established about 22 % better reliability and accurate more than the conventional
DRASTIC index modeling result. Lastly, using the model forecast method and the
artificial neural network (ANN) model technique, the predictive power accuracy of the
developed MLR recharge model was established. The applied results of the MLR
recharge modeling produced groundwater recharge rate prediction model map validated
with groundwater well yield data to gives 86 % accuracy. In addition, the estimated
recharge rate results for the area Alluvium rock, Devonian rock, Silurian rock and
Granitoid (igneous) rock using the MLR recharge models developed for the varying rock
types are 241.69 mm/yr, 247.63 mm/yr, 265.40 mm/yr and 247.17 mm/yr, respectively.
The obtained results suggest that the area recharge rate due to rainfall infiltration is a
function of the underlain geology. With the adopted diverse methodologies, multiple
decision making models that are very relevant to groundwater resources exploration and
management have been developed. The developed models are viable constituent for
producing groundwater hydrological database that can be explore for cost effective
groundwater resources exploitation and management in the study area and the country at
large.