Journal of Artificial Intelligence, Machine Learning and Soft Computing

ABSTRACT

Environmental monitoring is becoming very important due to rapid climate change, pollution, deforestation and loss of biodiversity across the world. Traditional monitoring techniques are often slow, expensive and limited in coverage. Artificial Intelligence (AI) offers new capabilities to analyze large volumes of environmental data coming from satellites, sensors, drones and IoT devices. AI models can detect patterns, predict environmental changes and assist in decision making for sustainable development. This paper presents a comprehensive review of how AI techniques are being used in environmental monitoring. It discusses machine learning, deep learning, computer vision and data analytics methods applied for air quality monitoring, water pollution detection, wildlife tracking, forest monitoring and climate prediction. The paper also highlights challenges such as data quality, computational cost and ethical concerns. Finally, future directions are discussed where AI can significantly support global environmental protection efforts.

KEYWORDS:  Artificial Intelligence, Environmental Monitoring, Machine Learning, Remote Sensing, IoT Sensors, Climate Change, Deep Learning