IoT Sensor Based Cross-Basin Natural Ecological Environment QualityMonitoring and Modeling Simulation with Artificial Intelligence RemoteSensing and GIS
DOI:
https://doi.org/10.69996/jsihs.2024014Keywords:
Hidden Markov Model (HMM), Internet of Things (IoT), Artificial Intelligence, Remote Sensing, Clustering, ClassificationAbstract
This study presents an integrated approach for monitoring and modelling the quality of crossbasin natural ecological environments using advanced techniques such as Markov Random Field Clustering Classification (MRF-CC), Geographic Information Systems (GIS), Hidden Markov Models (HMM), and remote sensing. With key ecological parameters including water quality, biodiversity, habitat suitability, and land cover types across various scenarios and locations within a study area. The simulation results from MRF-CC revealed the significant impacts of different environmental scenarios and management actions, with restoration efforts showing improvements in ecological quality, while pollution mitigation and urbanization pressures led to declines. The simulation results from MRF-CC revealed the significant impacts of different environmental scenarios and management actions. For example, restoration efforts (Scenario 1) improved water quality (pH 7.5), biodiversity index (0.88), and habitat suitability (0.78) compared to the baseline values (pH 7.2, biodiversity index 0.85, habitat suitability 0.75). Conversely, pollution mitigation (Scenario 2) and urbanization pressure (Scenario 8) resulted in declines, with water quality dropping to pH 7.0 and 6.9, biodiversity indices to 0.82 and 0.81, and habitat suitability to 0.72 and 0.71, respectively. GIS estimation provided spatial insights into ecological parameters, revealing variability across different locations. For instance, Point A (forest) exhibited a water quality index of 0.78, while Point D (urban) showed a lower index of 0.54. The integration of HMM offered probabilistic predictions of land cover dynamics, with probabilities ranging from 0.85 for forest at Point A to 0.45 for urban land cover at Point D.
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