About the Book
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.
Table 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
About the Author :
Hamid Reza Pourghasemi is a professor of watershed management engineering in the College of Agriculture, Shiraz University, in Iran. His main research interests are GIS-based spatial modelling using machine learning/data mining techniques in different fields such as landslides, floods, gully erosion, forest fires, land subsidence, species distribution modelling, and groundwater/hydrology. Professor Pourghasemi also works on multi-criteria decision-making methods in natural resources and environmental science. He has published over 230 peer-reviewed papers in high-quality journals and seven edited books for Springer and Elsevier and is an active reviewer for over 90 international journals. He was selected as one of the five young scientists under 40 by The World Academy of Science (TWAS 2019) and was a highly cited researcher in 2019 and 2020 Narges Kariminejad is a geomorphologist with about 10 years of work experience in field and laboratory-based soil erosion research in arid and semi-arid environments. She is also currently a researcher at the Department of Natural Resources and Environment Engineering in the College of Agriculture at Shiraz University, in Iran. Her research interests are in soil erosion, especially in rill, soil piping, and gully erosion. She has served as a guest scientist or visiting researcher at various research institutes and universities in different countries all over the world. Dr. Kariminejad has been a guest lecturer in difference courses, including quantitative geomorphology, spatial analysis and satellite imagery, plant ecology, and geostatistics. She has published more than 20 papers in international scientific journals.
