Classification metrics for anomaly detection.

BackgroundThe rapid development of urban systems and rising requirements for sustainable development lift resource management issues in smart communities. A fundamental problem for contemporary communities involves effectively using energy and water resources and waste management systems under environmental limitations. Artificial intelligence (AI) techniques at an advanced level deliver new methods that optimize resource management systems.ObjectiveThe research builds and examines a deep-learning framework that optimizes the management of smart community resources. The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. The system focuses on two main objectives, which include better forecasting precision, optimum resource distribution, and efficient detection of operational problems.MethodsResearch validation employed data from the Amsterdam Open Data Platform and Singapore Government Open Data Portal joined by crowdsourced platforms FixMyStreet and OneService. The preprocessing phase involved three stages, i.e., cleaning and normalization and feature engineering steps, before model training and testing phases. Predictive models received assessment based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². A comparison with traditional methods revealed the proposed approach delivered superior performance results.ResultsThe deep learning framework demonstrated superior performance, achieving an average reduction of 18.7% in resource consumption and a 16.2% reduction in operational costs. The models outperformed baseline methods, with LSTMs achieving an MAE of 1.8 for water demand prediction and autoencoders detecting anomalies with an F1-score of 95.5%.ConclusionDue to its effective capabilities, the proposed framework solves challenges in resource management for smart communities while showing the potential of AI-driven solutions for sustainable urban development. Research results demonstrate that integrating sophisticated deep-learning methods yields more significant potential for optimizing resource utilization while improving operational effectiveness.