AI-Based Waste Detection for Water Quality Monitoring in the Cisadane River: A Deep Learning Approach

Abstract

The rapid accumulation of waste in Indonesia's rivers, particularly the Cisadane River, seriously threatens water quality, ecosystem health, and public well-being. Traditional waste monitoring methods are inefficient and often fail to deliver timely data for effective interventions. This study addresses this gap by proposing an AI-based waste detection system for real-time water quality monitoring using deep learning techniques. A hybrid model integrating Convolutional Neural Network (CNN) and You Only Look Once version 7 (YOLO v7) was developed and tested on a dataset of 10,000 annotated images—60% organic and 40% inorganic waste—collected from the Cisadane River. The CNN model achieved a classification accuracy of 87%, a precision of 84%, a recall of 86%, and an F1-score of 85%. The YOLO v7 model demonstrated % detection accuracy of 82% with a processing speed of 20 frames per second. While mean Average Precision (mAP) was not directly calculated, the model's performance across key metrics supports its real-time applicability. This research offers a scalable and cost-effective approach for river waste monitoring and highlights the potential of AI in supporting sustainable environmental management in Indonesia.