The Niger Delta region, particularly Warri City, faces critical groundwater contamination resulting from petroleum exploration, industrial discharges, urbanization, and inadequate waste management practices. This study presents a Wireless Sensor Network (WSN)-based framework that integrates Internet of Things (IoT) sensors, cloud computing, machine learning, and geospatial analysis for real-time monitoring and prediction of groundwater pollutants. Forty-five sensor nodes were deployed across industrial, residential, agricultural, and wetland zones of Warri City, continuously monitoring physicochemical parameters, petroleum hydrocarbons, and heavy metals including lead (Pb), cadmium (Cd), chromium (Cr), and nickel (Ni) over an 18-month period. Predictive models comprising Random Forest (RF), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks were developed to forecast contamination trends. The WSN framework demonstrated high data transmission reliability and robust spatial hazard mapping capabilities. Results revealed elevated concentrations of Pb and Cd that exceeded World Health Organization (WHO) drinking water guidelines in several monitoring locations. While nickel contamination was detected, elevated concentrations were highly localized within industrial areas, remaining at safe baseline levels within residential zones. Total Petroleum Hydrocarbon (TPH) concentrations exhibited significant seasonal variation, reaching levels far above recommended environmental thresholds during the dry season. Among the predictive models evaluated, LSTM demonstrated superior forecasting performance with an R2 value of 0.91. Human health-risk assessment showed Hazard Index (HI) values exceeding safe limits across all population groups, with infants (HI=5.67) and pregnant women (HI=4.01) identified as the most vulnerable demographics. The findings underscore the severity of groundwater contamination in Warri City and demonstrate the potential of IoT-enabled WSN systems for continuous environmental monitoring, predictive analytics, and evidence-based groundwater management in the Niger Delta and similar industrialized regions.
Wireless Sensor Networks (WSN); Internet of Things (IoT); Groundwater Quality Monitoring; Heavy Metals; Lead (Pb); Cadmium (Cd); Chromium (Cr); Nickel (Ni); Total Petroleum Hydrocarbons (TPH); Machine Learning; Long Short-Term Memory (LSTM); Health Risk Assessment; Niger Delta; Warri City; Predictive Analytics.
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