Smart IoT-Based Environmental Monitoring Systems Using Artificial Intelligence

Abstract

Environmental monitoring is essential for protecting human health, ecosystem stability, agricultural productivity, and sustainable resource management. However, conventional monitoring approaches often depend on periodic sampling and manual assessment, which may limit the timely detection of rapidly changing environmental conditions. This study proposes an integrated Internet of Things (IoT) and Artificial Intelligence (AI) framework for smart environmental monitoring that combines distributed environmental sensors, wireless communication, edge/cloud computing, data preprocessing, AI analytics, and decision-support tools. The proposed framework continuously collects environmental parameters including temperature, relative humidity, particulate matter, soil moisture, pH, turbidity, electrical conductivity, and dissolved oxygen. AI-based analytical approaches are incorporated for anomaly detection, environmental classification, and forecasting of future environmental conditions. The framework can identify unusual environmental patterns, generate automated alerts, and provide predictive information to support timely management decisions. Its potential applications include smart agricultural irrigation management, air-quality monitoring, pollution hotspot identification, and continuous water-quality assessment. The integration of real-time sensing with AI-based prediction can transform environmental monitoring from a predominantly reactive approach into a real-time, predictive, and decision-oriented system. However, practical implementation requires appropriate sensor calibration, high-quality datasets, model validation, and field-based performance evaluation. The proposed framework provides a foundation for developing intelligent environmental monitoring systems capable of supporting sustainable agriculture, pollution management, and improved environmental decision-making.