Machine Learning Assisted Evaluation of Hybrid Constructed Wetlands Treating Multi-Source Wastewater

The review of literature confirms that Hybrid Constructed Wetlands (HCWs) represent an effective, sustainable, and low- energy alternative to conventional wastewater treatment systems. By integrating vertical and horizontal flow configurations, HCWs enhance pollutant removal through complementary aerobic and anaerobic mechanisms, improving the treatment of organic matter and nutrients. However, their efficiency is influenced by several interacting factors, including hydraulic loading rate, influent characteristics, substrate properties, vegetation type, and climatic conditions. The complexity and nonlinear behavior of these systems highlight the need for advanced analytical and predictive tools. Machine Learning (ML) techniques have demonstrated strong capability in modeling complex wastewater treatment processes, offering high predictive accuracy for effluent quality and operational performance. Algorithms such as Artificial Neural Networks, Random Forest, Support Vector Machines, and deep learning models provide data-driven solutions for performance evaluation and optimization. Nevertheless, limited research has integrated ML-based prediction, feature analysis, and multi- objective optimization specifically for Hybrid Constructed Wetlands treating multi-source wastewater. Future work should focus on developing integrated ML-based decision-support systems that combine real-time monitoring, predictive modeling, and optimization strategies for adaptive wetland management. Advanced ensemble and deep learning approaches can be explored to enhance time-series forecasting under dynamic influent conditions. Additionally, multi-objective optimization frameworks should be developed to simultaneously improve pollutant removal efficiency, reduce operational costs, and enhance energy sustainability. Long-term field validation, integration with IoT-based monitoring systems, and exploration of resource recovery and water reuse potential will further strengthen the practical implementation and sustainability of ML-assisted Hybrid Constructed Wetland systems.

1.          Quratulain Maqsood et al., “Revolutionizing water and wastewater treatment: Data-driven approaches for advanced solutions” Desalination and Water Treatment, Elsevier, Volume 324, 2025, 101432. https://doi.org/10.1016/j.dwt.2025.101432 2.          Yanbo Liu et al., “AI-driven solutions in wastewater treatment and agricultural reuse systems: A comprehensive review” Journal of Environmental Management, Elsevier, Volume 393, 2025, 127008. https://doi.org/10.1016/j.jenvman.2025.127008 3.          Muhammad Ishtiaq et al., “Two years monitoring and optimization of a large-scale constructed wetland in the Red Sea designed for 80,000 inhabitants” Journal of Cleaner Production, Elsevier, Volume 501, 2025, 145307. https://doi.org/10.1016/j.jclepro.2025.145307 4.          Malinowski Paweł et al., “Predicting organic matter and nutrient removal efficiency in a constructed wetland using data mining” Desalination and Water Treatment, Elsevier, Volume 321, 2025, 100926. https://doi.org/10.1016/j.dwt.2024.100926 5.          Usharani Rathinam    Krishnaswamy    et    al.,    “Floating treatment wetland with microbial fuel cell for removing pollutants, energy utilization, recovery of nutrients – Review on sustainable technology” Desalination and Water Treatment, Elsevier, Volume 323, 2025, 101264. https://doi.org/10.1016/j.dwt.2025.101264 6.          Yashasvi Trivedi et al., “Biochar potential for pollutant removal during wastewater treatment: A comprehensive review of separation mechanisms, technological integration, and process  analysis”  Desalination,  Elsevier,  Volume 600, 2025, 118509. https://doi.org/10.1016/j.desal.2024.118509 7.          Md. Abdur Rashid Sarker et al., “From data to decision: leveraging machine learning and water quality index for groundwater    quality           evaluation”    Sustainable           Water Resources Management, Springer,2025. https://doi.org/10.1007/s40899-025-01276-7 8.          Saad Aslam et al., “Machine learning applications in energy systems: current trends, challenges, and research directions” Energy Informatics, Elsevier,2025. https://doi.org/10.1186/s42162-025-00524-6 9.          Rakesh Kumar et al., “Harnessing biochar for nitrate removal from contaminated soil and water environments: Economic implications, practical feasibility, and future perspectives” Biochar, Springer,2025. https://doi.org/10.1007/s42773-025-00486-8 10.      Abhijeet Das et al., “Water quality index modelling and its application on artificial intelligence (AI) in conjunction with machine learning (ML) methodologies for mapping surface water potential zones for drinking activities” Das Discover Civil Engineering, Springer,2025. https://doi.org/10.1007/s44290-025-00298-6