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International Journal of Advance Scientific Research SCIENTIFIC IDEAS. INTERNATIONAL PERSPECTIVES.
ISSN 2750-1396 12 issues per year · Monthly
Crossref DOI: 10.37547/ijasr
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Articles Published:

AI-Driven Environmental Monitoring for Climate Change and Biodiversity Conservation

Vol. 6 No. 11 (2026): Volume 06 Issue 11 Pages 81-94
Authors
  • Dr. Ananya Verma Verma
    Department of Environmental Science, University of Kalyani, Kalyani, Nadia, West Bengal, India
DOI 10.37547/ ↗
RESEARCH OVERVIEW

Abstract

Climate change, biodiversity loss, ecosystem degradation, and the increasing frequency of environmental hazards have created an urgent need for intelligent, data-driven environmental monitoring systems. Traditional monitoring approaches often face limitations related to spatial coverage, temporal resolution, data integration, operational cost, and the ability to identify complex environmental patterns. Artificial intelligence (AI), machine learning (ML), Internet of Things (IoT) technologies, geospatial analytics, cloud computing, and big data platforms offer opportunities to improve environmental observation, prediction, and decision-making. This paper examines the role of AI-driven environmental monitoring in climate change assessment and biodiversity conservation, with particular attention to drought prediction, forest fire susceptibility, wetland water-level forecasting, habitat suitability modeling, marine ecosystem monitoring, land-cover classification, and environmental data integration. Existing research indicates that machine learning can support the identification of nonlinear environmental relationships, while remote sensing and geographic information systems can extend monitoring across large and inaccessible regions. IoT sensors and cloud-based infrastructures further enable continuous observation and the integration of heterogeneous environmental datasets. However, the effectiveness of these technologies depends on data quality, model generalizability, computational resources, interpretability, governance, and cooperation among environmental scientists, policymakers, and local communities. This paper develops a conceptual framework connecting environmental data acquisition, data integration, AI-based analysis, risk forecasting, and conservation action. It also discusses implementation challenges, ethical considerations, and future research priorities. The central argument is that AI should complement ecological knowledge and institutional decision-making rather than replace them. Responsible integration of intelligent monitoring systems can strengthen early-warning capabilities, improve conservation planning, and support more adaptive and sustainable environmental management.

Keywords

  • Artificial Intelligence
  • Machine Learning
  • Environmental Monitoring
  • Climate Change
  • Biodiversity Conservation
  • Remote Sensing
  • Internet of Things
  • Big Data Analytics
  • Ecological Modeling
  • Sustainable Development
SOURCES & CITATIONS

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