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GTNet: A Graph–Transformer Neural Network for Robust Ecological Health Monitoring in Smart Cities.

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Title: GTNet: A Graph–Transformer Neural Network for Robust Ecological Health Monitoring in Smart Cities.
Authors: Aldossary, Mohammad
Source: Mathematics (2227-7390); Jan2026, Vol. 14 Issue 1, p64, 43p
Subject Terms: ENVIRONMENTAL monitoring, SMART cities, URBAN ecology, ENVIRONMENTAL management, PREDICTION models, SUSTAINABILITY, TRANSFORMER models
Geographic Terms: MELBOURNE (Vic.)
Abstract: Urban towns and smart city governments face increasing challenges in maintaining ecological balance as urbanization, industrial activity, and climate dynamics evolve. The degradation of ecological gardens, biodiversity parks, and waterways adversely affects ecosystem stability, air and water quality, and community well-being. Conventional urban ecological systems rely on reactive assessment methods that detect damage only after it occurs, leading to delayed interventions, higher maintenance costs, and irreversible environmental harm. This study introduces a Graph–Transformer Neural Network (GTNet) as a data-driven and predictive framework for sustainable urban ecological management. GTNet provides real-time estimation of smart city garden health, addressing the gap in proactive environmental monitoring. The model captures spatial relationships and contextual dependencies among multimodal environmental features using Dynamic Graph Convolutional Neural Network (DGCNN) and Vision Transformer (ViT) layers. The preprocessing pipeline integrates Principal Component Aggregation with Orthogonal Constraints (PCAOC) for dimensionality reduction, Weighted Cross-Variance Selection (WCVS) for feature relevance, and Selective Equilibrium Resampling (SER) for class balancing, ensuring robustness and interpretability across complex ecological datasets. Two new metrics, Contextual Consistency Score (CCS) and Complexity-Weighted Accuracy (CWA), are introduced to evaluate model reliability and performance under diverse environmental conditions. Experimental results on Melbourne's multi-year urban garden datasets demonstrate that GTNet outperforms baseline models such as Predictive Clustering Trees, LSTM networks, and Random Forests, achieving an AUC of 98.9%, CCS of 0.94, and CWA of 0.96. GTNet's scalability, predictive accuracy, and computational efficiency establish it as a powerful framework for AI-driven ecological governance. This research supports the transition of future smart cities from reactive to proactive, transparent, and sustainable environmental management. [ABSTRACT FROM AUTHOR]
Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Mathematics (2227-7390); Jan2026, Vol. 14 Issue 1, p64, 43p
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  Data: <searchLink fieldCode="DE" term="%22ENVIRONMENTAL+monitoring%22">ENVIRONMENTAL monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22SMART+cities%22">SMART cities</searchLink><br /><searchLink fieldCode="DE" term="%22URBAN+ecology%22">URBAN ecology</searchLink><br /><searchLink fieldCode="DE" term="%22ENVIRONMENTAL+management%22">ENVIRONMENTAL management</searchLink><br /><searchLink fieldCode="DE" term="%22PREDICTION+models%22">PREDICTION models</searchLink><br /><searchLink fieldCode="DE" term="%22SUSTAINABILITY%22">SUSTAINABILITY</searchLink><br /><searchLink fieldCode="DE" term="%22TRANSFORMER+models%22">TRANSFORMER models</searchLink>
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  Data: Urban towns and smart city governments face increasing challenges in maintaining ecological balance as urbanization, industrial activity, and climate dynamics evolve. The degradation of ecological gardens, biodiversity parks, and waterways adversely affects ecosystem stability, air and water quality, and community well-being. Conventional urban ecological systems rely on reactive assessment methods that detect damage only after it occurs, leading to delayed interventions, higher maintenance costs, and irreversible environmental harm. This study introduces a Graph–Transformer Neural Network (GTNet) as a data-driven and predictive framework for sustainable urban ecological management. GTNet provides real-time estimation of smart city garden health, addressing the gap in proactive environmental monitoring. The model captures spatial relationships and contextual dependencies among multimodal environmental features using Dynamic Graph Convolutional Neural Network (DGCNN) and Vision Transformer (ViT) layers. The preprocessing pipeline integrates Principal Component Aggregation with Orthogonal Constraints (PCAOC) for dimensionality reduction, Weighted Cross-Variance Selection (WCVS) for feature relevance, and Selective Equilibrium Resampling (SER) for class balancing, ensuring robustness and interpretability across complex ecological datasets. Two new metrics, Contextual Consistency Score (CCS) and Complexity-Weighted Accuracy (CWA), are introduced to evaluate model reliability and performance under diverse environmental conditions. Experimental results on Melbourne's multi-year urban garden datasets demonstrate that GTNet outperforms baseline models such as Predictive Clustering Trees, LSTM networks, and Random Forests, achieving an AUC of 98.9%, CCS of 0.94, and CWA of 0.96. GTNet's scalability, predictive accuracy, and computational efficiency establish it as a powerful framework for AI-driven ecological governance. This research supports the transition of future smart cities from reactive to proactive, transparent, and sustainable environmental management. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: <i>Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/math14010064
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      – Code: eng
        Text: English
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        PageCount: 43
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      – SubjectFull: MELBOURNE (Vic.)
        Type: general
      – SubjectFull: ENVIRONMENTAL monitoring
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      – SubjectFull: SMART cities
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      – SubjectFull: URBAN ecology
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      – SubjectFull: ENVIRONMENTAL management
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      – SubjectFull: PREDICTION models
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      – SubjectFull: SUSTAINABILITY
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      – SubjectFull: TRANSFORMER models
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      – TitleFull: GTNet: A Graph–Transformer Neural Network for Robust Ecological Health Monitoring in Smart Cities.
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              M: 01
              Text: Jan2026
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              Y: 2026
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