Machine Learning in Geographical Information Systems (GISs)
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 175
Special Issue Editors
Interests: hydrogeology; groundwater; landslides modeling; artificial intelligence in geotechnical engineering
Special Issues, Collections and Topics in MDPI journals
Interests: numerical methods; geotechnical engineering; groundwater modeling; groundwater; landslides modeling; slope stability; engineering geology
Special Issues, Collections and Topics in MDPI journals
Interests: disaster management and practices; hydraulic engineering; watershed management; water resource conservation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue "Machine Learning in Geographical Information Systems (GISs)" focuses on the integration of machine learning techniques within GIS to address various challenges related to interdisciplinary fields. Machine Learning algorithms are applied to analyze big data sets within GIS frameworks, enabling enhanced prediction and assessment capabilities. Various Machine Learning models, including supervised and unsupervised learning, reinforcement learning, and deep learning, illustrate adaptability to diverse GIS applications such as land cover classification, remote sensing, spatial clustering, natural hazards, and predictive modeling. The synergy between Machine Learning and GIS offers novel approaches to handling complex spatial datasets, enhancing the efficiency and accuracy of geospatial analyses.
This Special Issue aims to encompass innovative methodologies and applications utilizing Machine Learning to explore case studies and real-world applications of Machine Learning in GIS.
Dr. Chih-Yu Liu
Prof. Dr. Cheng-Yu Ku
Dr. Yu-Jia Chiu
Guest Editors
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Keywords
- machine learning
- geographical information systems
- predictive modeling
- big data
- geospatial analysis
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