Sustainable Water Ecosystem Management in Data Science

Abstract

The water ecosystem are very much essential for agriculture, environmental balance and human health, by they are increasingly threats from climate variability, pollution and over – extraction. The traditional way of water monitoring methods, which may depends on manual sampling and the time – consuming for laboratory tests, it may often fail to provide the data rapidly enough for the timely intervention. Integrated data – driven framework for the water ecosystem management it can be integrated with Machine Learning (ML) models, decision support system, sensor network for real – time sensing and anomaly detection. It will be continuously collects the water quality and quantity data like dissolved oxygen, temperature, turbidity, water level and pH. It may be combines with geospatial and historical datasets for improving the analytical insights. In Predictive Modeling, LSTM and XG Boost algorithm are utilize to predict the water conditions, and the Isolation Forest method applied to detect anomaly in water quality data. A Water Health Score is introduced within the framework, and serving as an integrated measures of the ecosystem health. Experimental results using open datasets indicate high forecasting accuracy, timely alerts and anomaly detection, allowing stakeholders to take the proactive measures for potential contamination, resource shortage or unusual water level. The distinguished feature of the approach can lies in the end- end framework, it can combines with predictive analytics, real – time monitoring , anomaly detection and decision support in one scalable system.