Smart Contaminant Detection Approach For Enhancing The Reliability Of Groundwater Quality And Distribution Farmwork

Abstract

This study proposes a smart and scalable deep learning framework, GeoWaterNet, for intelligent contaminant detection and reliability-driven groundwater quality assessment within the context of sustainable water supply systems. Focused on the diverse hydrogeological conditions of Madhya Pradesh (MP), India, the study utilizes a five-year dataset of 1,000 groundwater samples collected from 21 spatially distributed locations to capture regional variability. GeoWaterNet leverages advanced feature extraction from critical hydrochemical parameters including major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺), anions (NO₂⁻ + NO₃⁻, CO₃²⁻, HCO₃⁻, Cl⁻, F⁻, SO₄²⁻), and physical indices such as pH, hardness, and electrical conductivity (EC).

The model architecture integrates multi-layered deep learning components optimized for tabular environmental data, delivering a high prediction accuracy of 93%, specificity of 94.57%, and sensitivity of 91.47%. To evaluate potability and distribution reliability, the Water Quality Index (WQI) is employed, revealing that only 22% of samples meet safe drinking standards, 63% fall into a conditionally safe category, and 15% are suitable exclusively for irrigation.

By combining intelligent contaminant profiling with predictive analytics, GeoWaterNet enhances the operational reliability of groundwater monitoring systems and supports data-informed decision-making for robust water distribution management. The proposed approach aligns with modern objectives for resilient, smart, and efficient water infrastructure across varying geographies.