- Pallavi S. Chakole
Assistant Professor, Department of Civil Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India.
chakole121pallu@gmail.com https://orcid.org/0000-0001-5300-0897 - Dr.A. Dhanalakshmi
Professor, Acharya Bangalore B School, Bengaluru, Karnataka, India.
dhanalakshmi555@gmail.com https://orcid.org/0000-0002-1446-1591 - Dr. Mamta Thakur
Assistant Professor, Department of Mathematics, Chaitanya Bharathi Institute of Technology, Hyderabad, India.
mamtathakur_maths@cbit.ac.in https://orcid.org/0000-0002-8204-6496 - Dr. Bechoo Lal
Associate Professor, Department of Computer science and Engineering, Konenru Lakshmaiah Education Foundation, vaddeswaram, AP, India.
drblalpersonal@gmail.co https://orcid.org/0000-0002-0225-1001 - Maher Ali Rusho
Department of Lockheed Martin Engineering Management, University of Colorado, Boulder, USA.
maher.rusho@colorado.edu https://orcid.org/0009-0001-5759-7042 - Anoop Dev
Centre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, India.
anoop.dev.orp@chitkara.edu.in https://orcid.org/0009-0001-1301-6891
Volume 10 - No: 3
AI-Assisted Bio-Engineering Approaches for Ecosystem Restoration: An Integrated Study of Pollution Control, Water Quality Prediction, and Biodiversity Sustainability
DOI: 10.28978/nesciences.1811171
Keywords: AI-assisted ecosystem restoration, bio-engineering, bioremediation, water quality prediction, biodiversity sustainability, machine learning, ecological modeling, environmental monitoring, predictive analytics, pollution control.
Abstract
Anthropogenic pollution of ecosystems, rapid climate change, and biodiversity losses have become an urgent issue in the world and need innovative and scaled-based solutions. The developments in artificial intelligence (AI), bio-engineering and environmental sensing technologies are currently providing unparalleled possibilities in rescuing ecological equilibrium in or affected health and natural ecosystems. This paper offers a novel and unified system that integrates AI-based pollution control, predictive water-quality, and bio-engineered remediation measures to aid the restoration of sustainable ecosystems. The research solution is based on deep-learning models, such as convolutional neural networks, long short-term memory networks, and transformers based on architectures, to be efficient in concluding the presence, classification, and forecasting of pollutant dynamics in terrestrial and aquatic ecosystems. Simultaneously, bio-engineering technologies like engineered microbial communities, hyperaccumulator plants, and optimum bioreactor designs have been used to hasten the degradation, absorption and fixation of contaminants. Moreover, AI-based models of biodiversity sustainability can be applied to measure the changes in distribution of species, habitat suitability and ecosystem resilience when subjected to environmental stressors of various levels. Based on experimental assessments and case studies, it is shown that AI combined with bio-engineered remediation improves the accuracy of identifying the source of the pollution by more than 30 times, it can be found to be more effective in the removal of contaminants, up to 38 times, and that it can maintain beneficial effects on biodiversity in the long-term, which can be achieved by optimised restoration strategies. The conclusions support the radical opportunities of AI-enhanced bio-engineering solutions to the restoration of fast, resilient, and scalable ecosystems. In addition, the research paper points at the major challenges such as the lack of data, ecological complexity, and ethical considerations and explains future research directions to serve the intelligent restoration ecology.