Advancing Water.... Prospects

Author : Shashank Mohan

Abstract :Water quality degradation threatens ecosystems, public health, and economic stability, necessitating efficient monitoring solutions. Traditional field-based methods are limited by cost and spatial-temporal constraints. This study explores the integration of remote sensing (RS) and machine learning (ML) to enhance large-scale water quality monitoring. ML models estimate key indicators such as chlorophyll-a, turbidity, and total suspended solids by leveraging multispectral/hyperspectral satellite data (e.g., Sentinel-2, Landsat-8) and UAV-based observations. The research employs regression models, deep learning architectures (CNNs, LSTMs), and hybrid approaches incorporating meteorological and land-use data. Case studies in Lake Taihu, the Mississippi River Basin, and Florida’s coastal regions demonstrate improved accuracy and cost-efficiency over traditional methods. However, challenges remain, including data limitations, model interpretability, and computational scalability. Future directions include advanced hyperspectral sensors, IoT-integrated monitoring, and explainable AI for regulatory applications. The RS-ML paradigm presents a transformative pathway toward proactive water resource management, aligning with global sustainability goals.

Keywords :Remote Sensing, Machine Learning, Water Quality Monitoring, Algal Blooms, Sustainable Development.

Conference Name :International Conference on Agricultural, Biotechnology, Biological and Biosystems Engineering (ICABBBE-25)

Conference Place Dubai, UAE

Conference Date 17th Apr 2025