Table of Contents

ABSTRACT

The Earth is undergoing significant transformations due to climate variations mainly driven by human activities, with climate change representing a deeper, long-term shift in weather patterns. In Egypt, the Nile River is the main freshwater source, provides agriculture, industry, and domestic water needs. This paper presents an innovative AI-based approach using Long Short-Term Memory (LSTM) networks to predict evaporation losses from Lake Nasser, advancing beyond traditional methods limited by changing climatic conditions. Utilizing extensive datasets encompassing climatic, hydrological, and geographical data, the model demonstrates enhanced forecasting precision critical for managing Lake Nasser, Egypt’s largest reservoir. LSTM model provides a dynamic prediction tool, shown to significantly improve forecast accuracy with Mean Absolute Errors of 0.2091 for CORDEX RCP 8.5 and 0.2095 for our predictions, and a coefficient of determination (R²) nearing 0.98, illustrating the model’s high reliability. This study not only introduces a robust model but also supports sustainable water resource management in response to evolving global climate challenges.

Keywords: evaporation prediction, LSTM, climate changes, water management, Lake Nasser, machine learning, sustainability