EXPLORING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON ENVIRONMENTAL MONITORING: UNVEILING OPPORTUNITIES AND OVERCOMING OBSTACLES

The world is a witness to a substantial environmental change driven by the increased world population and human activities, such as urbanization, converting land from forests to agriculture, nuclear power stations and construction of hydroelectric power (HEP) burning fossil fuels. In recent times, the development and integration of artificial intelligence (AI) in all fields has continued rapidly, and obtaining highly accurate models is on the agenda. This is primarily attributed to the progress of data mining techniques and machine learning (ML) algorithms which enable the extraction of valuable insights from large scale diverse and geo sources. The problem of the environment and its monitoring, one of the most important and urgent issues of today, can also be studied, and a new system can be established in the AI domain. The integration of AI into environmental monitoring systems is crucial for addressing critical global challenges, such as pollution control, land cover land use (LULC) change, biodiversity conservation, waterlogging susceptibility (WLS) and climate change mitigation. This paper presents a comprehensive overview of current AI applications in environmental monitoring, focusing on key areas such as air and water quality assessment, deforestation monitoring, and climate forecasting. Integrating the results of recent research, we identify emerging AI techniques, including machine learning, deep learning, and IoT-based approaches, that improve data collection, processing, and decision-making in real-time scenarios. Keywords: AI, Enviromental Monitoring System, water quality, air quality, deforestation monitoring.