Artificial Intelligence (AI) for Environmental Sustainability: A Concise Review of Technology Innovations in Energy, Transportation, Biodiversity, and Water Management

Asif Raihan; Arindrajit Paul; Md. Shoaibur Rahman; Samanta Islam; Pramila Paul; Sourav Karmakar

doi:10.56556/jtie.v3i2.953

Authors

Asif Raihan Institute of Climate Change, National University of Malaysia, Bangi 43600, Selangor, Malaysia
Arindrajit Paul Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, United States https://orcid.org/0009-0007-8231-9881
Md. Shoaibur Rahman Department of Agroforestry and Environment, Hajee Mohammad Danesh Science and Technology University, Dinajpur 5200, Bangladesh https://orcid.org/0009-0005-5355-2583
Samanta Islam Department of Environmental Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh 2220, Bangladesh
Pramila Paul University of Graz, Graz 8010, Austria
Sourav Karmakar University of Tartu, Tartu 50090, Estonia

DOI:

https://doi.org/10.56556/jtie.v3i2.953

Keywords:

Artificial intelligence, Technology innovations, Energy, Environment, Sustainability

Abstract

Artificial Intelligence plays a crucial role in addressing various environmental sustainability challenges through technological innovations in the fields of energy, transportation, biodiversity, and water management. Thus, the present study aims to present a concise review of Artificial Intelligence (AI) toward achieving environmental sustainability. The main areas of concentration in innovations in the field of energy encompass neural networks, expert systems, pattern recognition, and fuzzy logic models. Artificial Intelligence enables the creation of advanced prediction models for renewable energy production, enhancing the allocation of resources and management of the power grid. Moreover, computer vision and decision assistance have been used in the field of transportation. Additionally, the use of Artificial Intelligence and machine learning is growing to predict and enhance water resource conservation. Besides, machine learning and natural language processing techniques are being used in biodiversity research to predict ecological services. However, regular monitoring of initiatives is necessary to enhance environmental sustainability.