Quantitative Geomorphology in the Artificial intelligence Era
Applications of AI for Earth and Environmental Change
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Product details:
- Publisher Elsevier
- Date of Publication 1 August 2025
- ISBN 9780443300363
- Binding Paperback
- No. of pages410 pages
- Size 234x190 mm
- Language English 700
Categories
Long description:
Quantitative Geomorphology in the Artificial Intelligence Era: Applications of AI for Earth and Environmental Change focuses on bridging the gaps in this emerging discipline, it delves into the complex interplay between landforms and the processes that shape them, offering innovative solutions through AI and data-driven methods. The book addresses the standards, quality assessment of data, spatial and temporal analysis tools, and rigorous validation techniques in geomorphology. It uses computational intelligence as a pivotal tool alongside GIS, remote sensing, and other advanced technologies. Readers will find a holistic resource that fosters collaboration and knowledge exchange among geological fields, aiming to address geomorphological challenges, hazards, and solutions. By harnessing AI, GIS, remote sensing, machine learning, and geophysical techniques, it offers new dimensions to existing assessment methods and techniques.
MoreTable of Contents:
Part I. Foundational Quantitative Geomorphology: Introduction, theory and advances in quantitative geomorphology
1. Surface morphology and related Earth-surface processes
2. The integration of multiple data to understand the evolution of the landscape through large time scales (geological time) and the adaptation of species (living beings and plants) to such changes
3. Math for geomorphologists
4. Quantitative Geomorphology: mechanics and chemistry landscape
5. Digital Terrain Analysis: Principles and Applications
6. Quantitative Geomorphometry: Concepts, Software, Applications
7. Understanding landscape evolution and interactions with the environment through quantitative methods
8. Understanding of how processes are correlated and what the data can tell us about the correlations and feedback processes
9. Why and how to quantify processes
10. AI and big data in quantitative geomorphology
11. Theoretical, experimental, and quantitative geomorphology
Part-II - The application of quantitative techniques to hot topics in geomorphology
12. Past, present, and future environmental changes
13. Climate, tectonics, and regional structure, interactions between tectonic and surface processes
14. Anthropogenic geomorphology
15. Geomorphic hazards or Environmental multi-hazard
16. Geodiversity, bio geomorphology, predict species distribution, changes in biodiversity, and their adaptation to climate
17. Tectonics and/or anthropogenic processes
18. Effect of extreme methodological events or climate change on geomorphological processes and hazards
19. The role of quantitative Geomorphology in the development of urban and rural settlements
20. Landform classification
21. Scale, scaling laws and fractal applications to quantitative geomorphology
22. Use, advantages and limitations of big data and advanced technologies in quantitative geomorphology research
Part-III - Advanced Quantitative Geomorphology
23. Geomorphological indices
24. Landscape evolution models (LEM)
25. Quantitative geomorphology modelling, mapping, and its spatial-temporal variability 26. Applications to quantitative geomorphology to Risk management
27. Geophysical detection of surface and under surface landforms using AL ERA
28. Sensibility of the data quality in quantitative geomorphology
29. Impacts of climate change on geomorphological processes and hazards (e.g., UAV photogrammetry, TLS, ALS, etc.)
30. Application of high and very-high spatial and temporal resolution satellite multispectral and stereo imagery in geomorphic research (e.g., Worldview. Geoeye, Dove, etc.)
31. Survey of the degradability in the desert area using AL ERA
32. Quantitative analysis of watershed geomorphology using AL ERA
Part-IV -Tools in advanced quantitative geomorphology
33. GIS, remote sensing, and spatial modelling methods and applications, sedimentary records, and dating.
34. Multi-criteria GIS analysis in geomorphic susceptibility modelling
35. Artificial intelligence including Machine learning and Deep learning algorithms
36. Google Earth Engine and geomorphology
37. Aerial robotics including unmanned aerial vehicles (UAVs)
38. Internet of things (IoT), and analyzing big data (BD) in geomorphology
39. Geospatial analysis using AL ERAProcess modelling
40. Quantitative geomorphology: numerical modelling and coding.
41. Open-source software tools
42. Scripts, pictures or videos of features and links to open access tools/models
Quantitative Geomorphology in the Artificial intelligence Era: Applications of AI for Earth and Environmental Change
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