Maganti Syamala

Even though riverine ecosystems constitute the basis of ecological stability, biodiversity conservation, and provision of critical ecosystem services, there is a growing threat concerning the levels of aquatic pollution caused by industrial effluents, agricultural runoff, municipal waste discharge, and overt urban growth and expansion. Manual sampling and lab analysis based traditional methods of water quality monitoring are commonly slow, spatially limited, and incapable of defining the great dynamism of pollution signatures within flowing river systems. To overcome these shortcomings, this paper suggests a holistic AI integrated remote sensing system based on IoT to operate in real-time, high-resolution, spatio-temporal measures of aquatic pollution and ecosystem well-being from riverine settings. The framework combines these elements in a low-power wireless sensor network (WSNs) of continuous in-situ monitoring, multispectral/hyperspectral satellite data (such as Sentinel-2 and Landsat-8) on a platform, and unmanned aerial vehicle (UAV)-mounted optical and thermal already holds useful information to create a multi-source/ multi-scale environmental dataset. The feature extraction is being performed using the advanced artificial intelligence model such as deep neural networks (DNN), long short-term memory (LSTM) networks, gradient boosting algorithms, and spatio-temporal kriging; the predictive models, anomaly detection, and estimation of key water quality indicators such as pH, dissolved oxygen (DO), turbidity, total dissolved solids (TDS), nitrate concentration, and chlorophyll-a can be done. The unified system also includes analytics in the clouds and geospatial decision support tools to create pollution heatmaps, predict cases of contamination, and an analysis of the index of ecosystem health. As is evident in experimental validation with real world field data, the proposed framework is far more effective than the traditional method of monitoring in terms of prediction accuracy, latency, spatial coverage and also allows the ability to issue early-warnings. In general, the created AI-IoT-enabled remote sensing architecture provides an efficient, intelligent, and scalable framework of managing sustainable river basin, environmental policy control, and data-driven ecosystem security in response to emerging pressures caused by humans.