Abstract:
Groundwater vulnerability to salinity assessment serves as a measure of potential
groundwater salinity pollution in a target area. The study focused on modified
DRASTIC model to assess groundwater vulnerability to salinity, with machine
learning (random forest) to successfully predict groundwater saline sensitive
zones. However, research to predict groundwater salinity risk using machine
learning was limited. The aims of the study, to assess inherent sensitivity of
groundwater to pollution, determine spatial variations in vulnerability due to
additional factor of land use, to develop ground water salinity map based on
saline concentration and to predict potential ground water saline zones using
machine learning. The methods employed on the objectives, were the DRASTIC,
DRASTIC-LU incorporating the impact of anthropogenic influences, validation of
the DRASTIC-LU with groundwater quality data (EC as an indicator of salinity
levels) and implementation in machine learning model with the threshold value of
EC as the predictor class value. Groundwater salinity was increasingly recognized
as a significant environmental hazard, particularly in areas highly influenced by
the impact of anthropogenic activities such as mining, agricultural practices, and
different land use changes. Random forest classifier models were developed,
using depth to water (D), recharge (R), aquifer media (A), soil media (S),
topography (T), vadose zone (I) and hydraulic conductivity (C), on the basis of EC
class. To assess the performance of groundwater salinity risk model, training and
validation datasets were evaluated using the ROC curve and gained accuracy of
94%. The results revealed that approximately 20% of the study area fell within the
high salinity potential, and around 50% exhibited moderate salinity risk while 30%
very low to salinity risk concentration. Random forest algorithm could serve as
efficient technique for evaluating and managing groundwater resources. The
findings underscored relatively poor groundwater quality in the Busire, with
excessive aquifer exploitation by the agricultural sector and infiltration of urban
sewage and industrial waste identified as the primary causes of groundwater
salinity. The implications of these findings were crucial for devising strategies and
implementing preventive measures to mitigate water resource vulnerability and
associated health risks in Busitema Subcounty.
