Smart Environmental Engineering for Sustainable Aquatic Resource Management Using IoT Sensors, Satellite Data Fusion, and Machine Learning Analytics

Abstract

The adequate management of the aquatic system which includes rivers, lakes, reservoirs, wetlands, and coastal areas will need the continuous and high-resolution monitoring of the environment that will be able to handle the fast hydrological and ecological shifts. Conventional field methods of sampling offer poor spatial and temporal resolution, and they frequently do not reveal early pollution incidences, predict ecological hazards, or assist data-driven resources optimization. This paper will introduce an interdisciplinary smart environmental engineering paradigm that will combine Internet of Things (IoT) sensor networks, multispectral and synthetic aperture radar (SAR) satellite remote sensing, and machine learning (ML) analytics to allow real-time, predictive, and adaptive management of aquatic resources. In the given methodology, a hierarchical data fusion architecture is used to bring the high-frequency measurements of the in-situ sensors in harmony with the big data measurements of the satellites to improve the spatial-temporal resolution and interpretability of the environment. Various ML architectures, such as the Random Forest (classification), LSTM (time-series prediction), CNN-based spatial models (detecting the harmful algae bloom), and physics-informed neural networks (PINNs) (making predictions based on hydrodynamics) were tested to determine their efficiency involved in the forecasting of water quality parameters, assessing the pollution sources, and defining the habitat health. A pilot application of the integrated system in an actual freshwater lake showed that the integrated system is more effective at the prediction accuracy level (27 percent improvement), spatial mapping reliability, and a shorter (41 percent less) time to detect contaminants than traditional monitoring approaches. The results indicate the potential of integrating IoT with satellites and machine learning to enable a flexible, robust, and smart system of monitoring that can ultimately contribute to the active management of the environment, reinforce the methods of climate change adaptation, and help to achieve the sustainable preservation of water resources.