INTEGRATING ARTIFICIAL NEURAL NETWORKS AND GEOGRAPHIC INFORMATION SYSTEMS FOR ENVIRONMENTAL DATA ANALYSIS AND PREDICTION
DOI:
https://doi.org/10.11113/aej.v16.24819Keywords:
geographic information systems, machine learning, deep learning, environmental monitoring, and big data processing.Abstract
The relevance of the research lies in the need to apply modern methods of spatial environmental data analysis, particularly artificial neural networks with geographic information systems, to address global environmental issues like climate change, biodiversity loss, and resource degradation. The study aimed to investigate the potential of artificial neural networks and geographic information systems for analysing spatial environmental data, addressing the development of effective methods for processing and interpreting environmental information. The research developed methods for integrating artificial neural networks and geographic information systems to monitor ecosystems, assess environmental risks and improve environmental sustainability. Modern deep learning methods, including convolutional and recurrent neural networks, along with geographic information systems for visualization and modeling, enhance the analysis of environmental changes, pollution prediction, and natural resource management. The results show that the integration of these technologies helps to effectively solve environmental monitoring tasks, including controlling water and air pollution, studying the impact of climate change on ecosystems and predicting agricultural sustainability. The study emphasised the need to continue developing and applying artificial neural networks and geographic information systems to solve environmental problems and ensure sustainable management of natural resources and environmental protection.
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