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This study presents the application of deep machine learning techniques, specifically deep neural networks, to predict water levels in urban wetlands in Colombo, Sri Lanka. It compares traditional relationships between water levels and environmental factors with temporal relationships using feed forward neural networks (FFNN) and long short-term memory (LSTM) networks, finding that LSTM outperforms FFNN. The research highlights the importance of accurate water level predictions for wetland management and fl

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rhea9778

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EN