Smart Bioremediation: Role Of Artificial Intelligence-Based Monitoring and Control of Aquatic Plant Systems for Wastewater Treatment

Aayushi Sharma; Mohammad Faheem

doi:10.69968/ijisem.2026v5i3136-152

Authors

Aayushi Sharma PhD Research Scholar Botany (Life Science), Glocal University Mirzapur pole, Distt- Sharanpur
Mohammad Faheem Assistant Professor, School of Life & Allied Health Sciences Glocal University, Saharanpur Uttar Pradesh - 247121

DOI:

https://doi.org/10.69968/ijisem.2026v5i3136-152

Keywords:

Artificial Intelligence, Machine Learning, Wastewater Treatment, Bioremediation, Aquatic Plants, Optimisation Methods, Algorithms, AI-powered

Abstract

Significant technological developments in this field have been made possible by the idea of using wastewater as a replacement for scarce water supplies and to protect the environment, which has produced a wealth of physical data, including chemical, biological, and microbiological information. After looking at this data, wastewater treatment systems become more understandable. Several studies use "machine learning (ML) algorithms" as a proactive approach to tackle issues and forecast how these processing systems will function while using gathered experimental data. This article's objective is to extract the most widely used "machine learning models" from scientific publications in the "Web of Science" database using textual analysis techniques, then examine their applicability and evolution over time. This will provide a comprehensive overview of articles that address the application of artificial intelligence (AI) to address issues in "wastewater treatment technologies", as well as a global scientific follow-up. According to an analysis of research publications, the machine learning models most commonly used in the wastewater treatment domain are "Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), Linear Regression (LR), Adaptive Neuro-Fuzzy Inference System (ANFIS), Decision Tree (DT), and gradient boosting". The findings show that the primary publishers of publications on this study issue are developed countries. Finally, because machine learning requires a large amount of high-quality data, it helps with wastewater treatment. Furthermore, machine learning models' limited interpretability makes it more challenging to comprehend the underlying mechanisms and decisions in wastewater treatment. The AI algorithm for wastewater treatment is also examined in terms of the explainability of data-driven models for transfer learning and reinforcement learning, as well as other significant voids and future prospect. Enhancing energy efficiency, adhering to increasingly strict water quality laws, and optimising resource recovery prospects are just a few of the many issues associated with wastewater treatment. The operational and economic performance of “wastewater treatment plants (WWTPs)” has improved as a result of computational models' growing recognition as effective solutions for solving these difficulties. The paper also covers the use of several "AI algorithms" in "wastewater treatment plants (WWTPs)", such as identifying anomalies, optimising energy utilisation, and forecasting WWTP effluent characteristics and wastewater intakes.

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