Applications of artificial intelligence in water pollution prediction and management for environmental sustainability in conjunction with Egypt Vision 2030

Summary

This project presents an advanced, interdisciplinary framework for monitoring water pollution prediction and management in the Nile River by integrating artificial intelligence (AI) techniques with classical mathematical modeling. Building on previous research that focused on analytical and numerical solutions of advection-dispersion and pollutant dynamics equations, this project attempts to develop AI-based models to enhance the accuracy of pollutant concentration predictions. A hybrid model will be developed, combining the realism of physics-based differential equation models with the flexibility of machine learning and deep learning algorithms to enable precise forecasting and early anomaly detection. Optimization algorithms will be employed to select effective, time- and cost-efficient remediation strategies such as aeration or chemical treatment. The model will also simulate various intervention scenarios under different hydrological and climatic conditions. Ultimately, the project aims to deliver an intelligent, adaptive, and scalable system to support environmental decision-makers, contributing to improved water quality, pollution reduction, and the achievement of sustainable development goals. This project aligns with the strategic vision of Ain Shams University by promoting interdisciplinary problem-driven research that addresses national challenges.

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List of Publications from the Project

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