Zhao Yongyan
School of Humanities and Law, Harbin University, Harbin, Heilongjiang, China.
PLoS One. 2025 Aug 7;20(8):e0329492. doi: 10.1371/journal.pone.0329492. eCollection 2025.
The 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.
The 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.
Research 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.
The 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%.
Due 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.
城市系统的快速发展以及对可持续发展的要求不断提高，凸显了智能社区中的资源管理问题。当代社区面临的一个基本问题是在环境限制下有效利用能源、水资源和废物管理系统。先进的人工智能（AI）技术提供了优化资源管理系统的新方法。
本研究构建并检验了一个优化智能社区资源管理的深度学习框架。该框架利用长短期记忆（LSTM）网络处理时间数据，卷积神经网络（CNN）进行空间分析，自动编码器进行异常检测。该系统专注于两个主要目标，即提高预测精度、优化资源分配以及有效检测运营问题。
研究验证采用了来自阿姆斯特丹开放数据平台和新加坡政府开放数据门户的数据，并结合了众包平台FixMyStreet和OneService。在模型训练和测试阶段之前，预处理阶段包括三个步骤，即清洗、归一化和特征工程步骤。预测模型基于平均绝对误差（MAE）、均方根误差（RMSE）和R²进行评估。与传统方法的比较表明，所提出的方法具有更好的性能结果。
深度学习框架表现出卓越的性能，资源消耗平均减少了18.7%，运营成本降低了16.2%。这些模型优于基线方法，LSTM在预测用水需求时的MAE为1.8，自动编码器检测异常的F1分数为95.5%。
由于其有效能力，所提出的框架解决了智能社区资源管理中的挑战，同时展示了人工智能驱动的解决方案对可持续城市发展的潜力。研究结果表明，整合复杂的深度学习方法在优化资源利用和提高运营效率方面具有更大的潜力。
