Deep Learning Framework for Real-Time Detection of Chemical Spills in River Water Using Remote Sensing Imagery

Abstract

Rapid detection of chemical spills in river systems is essential for minimizing environmental damage, protecting aquatic ecosystems, and supporting timely emergency response actions. Conventional water quality monitoring methods often suffer from limited spatial coverage and delayed detection capability, making them insufficient for real-time contamination assessment in dynamic river environments. This study presents a deep learning framework for real-time detection of chemical spills in river water using remote sensing imagery under varying environmental and atmospheric conditions. The proposed framework integrates remote sensing data acquisition, image preprocessing, geographic information systems, and deep learning-based image analysis to automatically identify and classify contamination events in surface water bodies. Multispectral and hyperspectral remote sensing imagery were utilized to capture variations in water surface characteristics associated with chemical contamination, including spectral reflectance anomalies, turbidity changes, and surface dispersion patterns. Convolutional neural network architectures and advanced feature extraction techniques were employed to learn complex spatial and spectral relationships for accurate spill detection and classification. Experimental and simulation analyses demonstrated high detection accuracy, rapid response capability, and strong adaptability to varying river flow conditions and atmospheric interference compared to conventional image processing methods. The developed framework also enabled real-time spatial mapping of contaminant spread and supported predictive assessment of downstream pollution movement. Comparative evaluation revealed significant improvements in environmental surveillance efficiency, automated anomaly detection, and operational responsiveness for water quality monitoring applications.