APPLICATION OF ELECTRICAL RESISTIVITY TECHNIQUES FOR MAPPING GROUNDWATER POLLUTION USING RANDOM FOREST MACHINE LEARNING IN AGBABU, SOUTHWESTERN NIGERIA

Abstract

The potential groundwater pollution zones within Bitumen impregnated Agbabu community an agglomerated town in Odigbo Local Government was mapped using electrical resistivity methods with Machine learning regression to produce a potential vulnerability map of the area. The Electrical resistivity techniques consisted of 2D wenner imaging and vertical electrical sounding (VES) techniques were conducted along the established traverses. Water samples from wells within the area were subsequently collected for physico-chemical studies. Twenty (20) Vertical Electrical Sounding (VES) data were acquired at different locations. Results of the depth sounding showed that the KQ and HA were the dominant type-curves constituting about 90% of the curve types obtained. Four to five geo-electric/geologic layers were delineated with Bitumen impregnated layer found within the third and fourth layer with resistivities ranging from 86 to 255 Ωm. Results from the 2D Werner and geoelectric sequencing showed that the underlaying bitumen Impregnated layer was overlain by a protective clay layer which has some discontinuities and weak zones in Traverses 1 and 4 indicative of the most vulnerable zone(s) in terms of groundwater pollution while Traverses 2 and 3 are less vulnerable. Five (5) geophysically independent variables which include: longitudinal conductance, transverse resistance, hydraulic conductivity, depth to top of bitumen and hydraulic resistance were derived from the VES interpretated results. These variables which represent intrinsic properties of the groundwater quality were computed, mapped and fed into the Random Forest Machine Learning Tool. These variables were permuted and the effect of the out of bag classification was measured. Longitudinal Conductance was ranked highest with a variable importance of 0.51. The model used 75% of the input dataset to train the model while 25% was used to test the trained model, the model trained from this subset had a mean square error (MSE) of 0.0982. The resultant output predicted values which ranged from 0.7251 to 3 2.83836. The predictive vulnerable map showed that the northern and the central part trending north east were regions predicted to be the most vulnerable regions while the southern part of the study area was predicted to be the least vulnerable area.