Quantitative Geomorphology in the Artificial intelligence Era

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.

• Part I. Foundational Quantitative Geomorphology: Introduction, theory and advances in quantitative geomorphology 1. Surface morphology and related Earth-surface processes2. 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 changes3. Math for geomorphologists4. Quantitative Geomorphology: mechanics and chemistry landscape5. Digital Terrain Analysis: Principles and Applications6. Quantitative Geomorphometry: Concepts, Software, Applications7. Understanding landscape evolution and interactions with the environment through quantitative methods8. Understanding of how processes are correlated and what the data can tell us about the correlations and feedback processes9. Why and how to quantify processes10. AI and big data in quantitative geomorphology11. Theoretical, experimental, and quantitative geomorphologyPart-II - The application of quantitative techniques to hot topics in geomorphology12. Past, present, and future environmental changes13. Climate, tectonics, and regional structure, interactions between tectonic and surface processes14. Anthropogenic geomorphology15. Geomorphic hazards or Environmental multi-hazard16. Geodiversity, bio geomorphology, predict species distribution, changes in biodiversity, and their adaptation to climate17. Tectonics and/or anthropogenic processes18. Effect of extreme methodological events or climate change on geomorphological processes and hazards19. The role of quantitative Geomorphology in the development of urban and rural settlements20. Landform classification21. Scale, scaling laws and fractal applications to quantitative geomorphology22. Use, advantages and limitations of big data and advanced technologies in quantitative geomorphology researchPart-III - Advanced Quantitative Geomorphology23. Geomorphological indices24. Landscape evolution models (LEM)25. Quantitative geomorphology modelling, mapping, and its spatial-temporal variability 26. Applications to quantitative geomorphology to Risk management27. Geophysical detection of surface and under surface landforms using AL ERA28. Sensibility of the data quality in quantitative geomorphology29. 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 ERA32. Quantitative analysis of watershed geomorphology using AL ERAPart-IV –Tools in advanced quantitative geomorphology33. GIS, remote sensing, and spatial modelling methods and applications, sedimentary records, and dating.34. Multi-criteria GIS analysis in geomorphic susceptibility modelling35. Artificial intelligence including Machine learning and Deep learning algorithms36. Google Earth Engine and geomorphology37. Aerial robotics including unmanned aerial vehicles (UAVs)38. Internet of things (IoT), and analyzing big data (BD) in geomorphology39. Geospatial analysis using AL ERAProcess modelling40. Quantitative geomorphology: numerical modelling and coding.41. Open-source software tools42. Scripts, pictures or videos of features and links to open access tools/models