Development of a Machine Learning Model That Uses Mine Influents to Soil and Aquarium Water to Predict Future Changes

The increasing impact of mining activities on aquatic ecosystems has raised serious concerns regarding the accumulation of heavy metals in water bodies, which poses significant risks to fish survival and overall aquaculture sustainability. In regions near mining operations, influents containing metals such as copper (Cu), iron (Fe), and cobalt (Co) can leach into soil and water systems, disrupting water quality. This study was conducted to monitor and predict the physicochemical dynamics of water influenced by mining activities. In-situ measurements of key water quality parameters including pH, Cu, Fe, and Co were carried out using a multi-parameter sensor device in both soil and aquarium water settings to reflect environmental and controlled conditions. The observed concentrations revealed substantial deviations from the optimal levels necessary for healthy aquatic life. To address this, a machine learning (ML) model was developed using the measured influents as input variables to predict future changes in water quality. The predictive model demonstrated high accuracy and potential for real-time application in aquaculture management. Furthermore, linear regression analysis was employed to quantify the relationships between the selected physicochemical parameters and the ideal thresholds for aquatic health, offering deeper insight into their influence on ecosystem stability. The integration of ML for forecasting water quality represents a novel approach to proactive aquaculture monitoring and management, particularly in mining-influenced environments. This research contributes to the growing need for intelligent, data-driven tools in environmental monitoring and supports efforts to mitigate the adverse effects of industrial pollution on aquatic life.