Cross-basin natural ecological environment quality monitoring and modelling simulation based on artificial intelligence remote sensing and GIS

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Zongbo Liu


This study presents an innovative approach to monitoring and modelling the quality of natural ecological environments across multiple basins. Harnessing the power of artificial intelligence (AI), remote sensing technologies, and geographic information systems (GIS), this research aims to provide a comprehensive understanding of environmental dynamics and trend. The methodology integrates various AI techniques, including machine learning algorithms and neural networks, with high-resolution remote sensing data to extract valuable information about ecological parameters such as land cover, vegetation health, water quality, and biodiversity. GIS is employed as a spatial analytical tool to organize and visualize the vast amount of geospatial data collected from different basins. Through the implementation of advanced modelling and simulation techniques, this study seeks to forecast the future trajectories of ecological changes and assess the potential impacts of anthropogenic activities, climate change, and natural disasters on basin ecosystems. By simulating different scenarios, policymakers and stakeholders can make informed decisions to promote sustainable resource management and conservation strategies.

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