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01:43Sustainable Development
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As the human population continues to grow and use resources, we must be mindful of our planet’s natural limits. Sustainable development provides a pathway to maintain and improve human life now while also ensuring that future generations will have the resources that they need. The long-term success of sustainability efforts rests on understanding the interplay between human actions and ecological systems.
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01:26Design Example: Sustainability in Concrete Building
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As the construction industry moves towards more eco-friendly practices, concrete's adaptability and its ability to incorporate sustainable features make it a key material in the drive towards greener building solutions.
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01:29Applications of GIS: Disaster Management and Emergency Response
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02:12Short-distance Transport of Resources
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01:21Cognitive Learning
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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01:29Levels of Use of a GIS
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Updated: Sep 12, 2025
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Advancing smart communities with a deep learning framework for sustainable resource management.
1School of Humanities and Law, Harbin University, Harbin, Heilongjiang, China.
Plos One
|August 7, 2025
Summary
This study introduces an AI framework for smart community resource management, reducing consumption by 18.7% and costs by 16.2%. It optimizes energy, water, and waste systems using deep learning for sustainable urban development.
Area of Science:
- Urban Planning and Resource Management
- Artificial Intelligence and Machine Learning
- Sustainable Development
Background:
- Smart communities face challenges in sustainable resource management (energy, water, waste) due to urban growth.
- Advanced Artificial Intelligence (AI) techniques offer novel solutions for optimizing resource management systems.
Purpose of the Study:
- To develop and evaluate a deep-learning framework for optimizing smart community resource management.
- To enhance forecasting accuracy, optimize resource distribution, and detect operational issues efficiently.
Main Methods:
- The framework integrates Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and autoencoders.
- Data from open data platforms (Amsterdam, Singapore) and crowdsourced data were utilized.
- Model performance was assessed using MAE, RMSE, and R², with comparisons to traditional methods.
Main Results:
- The deep-learning framework achieved an 18.7% reduction in resource consumption and a 16.2% decrease in operational costs.
- LSTM models showed an MAE of 1.8 for water demand prediction.
- Autoencoders successfully detected anomalies with a 95.5% F1-score.
Conclusions:
- The proposed AI framework effectively addresses smart community resource management challenges.
- Integrating advanced deep-learning methods optimizes resource utilization and enhances operational efficiency for sustainable urban development.