Development of an IoT and AI-Integrated Web Platform for Real-Time River Water Quality Monitoring in Pahang River

Abstract

River water quality monitoring is essential for supporting environmental protection and early decision-making. However, IoT-based monitoring systems often generate large volumes of sensor data that are stored but not fully transformed into actionable information. This study presents the design and implementation of iSENS-Air, an AI-driven web-based decision support system for real-time river water quality monitoring in the Pahang River area. The system was developed for four monitoring locations, namely Bilut, Telum, Kechau, and Semantan. IoT sensors collect nine water quality parameters, including turbidity, biochemical oxygen demand, dissolved oxygen, chemical oxygen demand, ammonia, total dissolved solids, conductivity, oxidation-reduction potential, and pH. The collected data are stored in a cloud environment and accessed through API-based data retrieval. The platform integrates data preprocessing, water quality classification, risk identification, historical analysis, and AI-generated insight using the OpenAI API. The web dashboard was developed using Next.js to present real-time parameter cards, daily classification summaries, monitoring maps, historical data, and recommended actions. The implemented system demonstrates how raw sensor data can be transformed into interpretable information for river water quality monitoring and preliminary decision support.