AI-Optimized Fungal Bioremediation: A Framework for Stabilizing Riparian Zones and Mining Tailings

Abstract River ecosystems worldwide face severe degradation from erosion, mining pollution, and climate-induced changes, threatening livelihoods in agriculture, fishing, tourism, and commerce. This study proposes AI-driven mycoremediation strategies, leveraging fungal networks to remediate eroded riverbanks and recycle mining byproducts into nutrient-rich substrates. Degraded landscapes specifically eroded riparian zones and toxic mining tailings, present chronic environmental and economic challenges. Traditional engineering solutions are often resource-intensive and lack long-term ecological circularity. Mycoremediation—the utilization of fungi for environmental restoration—offers a biological alternative, yet its field-scale efficacy is often limited by environmental variability and unpredictable fungal behavior. This article proposes a novel framework: AI-Optimized Fungal Bioremediation (AOFB). By integrating Artificial Intelligence (AI) predictive modeling, real-time sensing, and species-specific mycelial properties, this framework aims to automate and optimize the dual processes of ecological stabilization and pollutant decontamination. We outline the technical components of AOFB and discuss its potential to transform environmental liabilities into regenerative ecosystems suitable for diverse land uses.