Development of the ‘AME’ AI-Based Water Quality Analysis Support System

Overview

At wastewater treatment facilities, it is crucial to analyze the characteristics of incoming sewage and detect pollutant inflows in advance to design efficient treatment processes. Conventional water quality management relies on manual analysis methods, making real-time response difficult and hindering swift action when pollutants enter the system. This project aims to establish a real-time monitoring and automated response system utilizing AI- based predictive models and IoT sensors.

Project description

This system analyzes water quality in real time at public sewage treatment facilities and detects pollutant inflows in advance through AI-based prediction.

• Development of AI-based Water Quality Prediction Model – Builds an AI model that predicts water quality changes using public data and real-time sensor data. Applies machine learning-based analysis technology to improve prediction accuracy for key water quality indicators (BOD, COD, SS, TN, TP).
• Real-time Water Quality Analysis and Monitoring – Utilizes IoT-based sensors to measure the quality of inflowing sewage in real time and immediately sends alerts to managers upon detecting abnormal signs. This enables early identification of factors causing water quality deterioration and allows for swift action.
• Big Data-Based Process Optimization – Integrates historical and real-time data to derive process operation optimization strategies. Automatically adjusts wastewater treatment processes based on AI analysis results to reduce energy consumption and enhance treatment efficiency.
• Cloud-Based Analysis and Visualization Platform Implementation – The system is designed to display AI analysis results in real time on a cloud-based visualization dashboard. This enables administrators and operators to instantly grasp water quality status through an intuitive UI and take immediate action.

Key technological features

AI-Based Water Quality Prediction Model: Building AI learning models using public data and real-time sensor information for predicting water quality trends Real-Time Water Quality Analysis and Monitoring: IoT- based data collection and analysis Big Data-Based Process Optimization: Deriving optimal water quality management strategies by integrating historical and real-time data Cloud-Based Analysis and Visualization Platform: Real-time data visualization and automated alert system implementation

Expected outcomes

This system will enhance the operational efficiency of wastewater treatment facilities and accelerate the digital transformation of public wastewater management. Furthermore, the AI-based automated water quality management system is expected to facilitate environmental pollution prevention and compliance with legal standards. Real-time response enabled through AI analysis predicting water quality factors when issues arise, such as through monitoring of water quality risk areas.

Launch year: 2024

Attribution

Case study courtesy of the National Information Society Agency, Republic of Korea, Apolitical's Content Partner. Sourced from Use Cases of Public AI Service, Vol.1,  https://eng.nia.or.kr/site/nia_eng/ex/bbs/View.do?cbIdx=31975&bcIdx=28997&parentSeq=29048