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Quantitative Geomorphology in the Artificial intelligence Era
◆日本地質学会第132年学術大会セール 開催中！：2025年10月24日(金)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 未刊 ご予約承ります。 Title: Quantitative Geomorphology in the Artificial intelligence Era Subtitle: Applications of AI for Earth and Environmental Change Publisher: Elsevier ISBN: 9780443300363 Cover: PAPERBACK Date: 2025年12月 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. 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
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