Professor Pradhan is a globally recognized expert in geospatial analytics and artificial intelligence applications in Earth and environmental sciences. Currently a Distinguished Professor at the University of Technology Sydney (UTS), Australia, he also leads the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). With a PhD in GIS-based modeling, Prof. Pradhan has over two decades of experience in spatial data science, remote sensing, natural hazard modeling, and environmental monitoring. He has been listed among the world's top 2% scientists by Stanford University and received numerous international awards, including from IEEE and Elsevier. A Fellow of the Royal Geographical Society (FRGS), he also serves on editorial boards of several top-tier journals. His research integrates geospatial AI and deep learning for disaster risk reduction, land use planning, and sustainability. Daichao Sheng is a distinguished professor and the head of School of Civil and Environmental Engineering. He has developed an internationally recognized profile in computational geomechanics including soft computing, unsaturated soils, geo-risk analysis and transport geotechnics. He has published 300+ peer-reviewed papers and two books, including 200+ papers in top geotechnical and computational mechanics journals. These publications now attract 1400+ citations per annum, with an H-Index of 48 in Scopus. His track record places him easily within the top handful of geomechanics professionals of his age worldwide. He has collaborated widely with Australian and international researchers in his field Xuzhen He is a senior lecturer at UTS School of Civil and Environmental Engineering. He is an early career researcher and completed his undergraduate and PhD training at the world’s top universities (Tsinghua for his BSc and Cambridge for his PhD) and was awarded the John Winbolt Prize and the Raymond and Helen Kwok Scholarship from Cambridge University. He was awarded the Australian Research Council Discovery Early Career Researcher Award in 2021. His research interest lies mainly in computational geomechanics, and he has published 30+ high-quality journal papers in these areas.

• Part 1: Machine learning methods and connections between different parts. 1. Machine learning methods2. Connections between studies across different scales3. Summary and outlookPart 2: Machine learning in microscopic modelling of geo-materials.4. Machine-learning-enabled discrete element method5. Machine learning in micromechanics based virtual laboratory testing6. Integrating X-ray CT and machine learning for better understanding of granular materials7. Summary and outlookPart 3: Machine learning in constitutive modelling of geo-materials. 8. Thermodynamics-driven deep neural network as constitutive equations9. Deep active learning for constitutive modelling of granular materials10. Summary and outlookPart 4: Machine learning in design of geo-structures. 11. Deep learning for surrogate modelling for geotechnical risk analysis12. Deep learning for geotechnical optimization of designs13. Deep learning for time series forecasting in geotechnical engineering14. Summary and outlookPart 5: Machine learning in geo-risk susceptibility mapping for regions of various sizes. 15. Deep learning and ensemble modeling of debris flows, mud flows and rockfalls.16. Integrating machine learning and physical-based models in landslide susceptibility and hazard mapping.17. Explainable AI (XAI) in landslide susceptibility, hazard, vulnerability and risk assessment.18. New approaches for data collection for susceptibility mapping19. Summary and outlook

Professor Pradhan is a globally recognized expert in geospatial analytics and artificial intelligence applications in Earth and environmental sciences. Currently a Distinguished Professor at the University of Technology Sydney (UTS), Australia, he also leads the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). With a PhD in GIS-based modeling, Prof. Pradhan has over two decades of experience in spatial data science, remote sensing, natural hazard modeling, and environmental monitoring. He has been listed among the world's top 2% scientists by Stanford University and received numerous international awards, including from IEEE and Elsevier. A Fellow of the Royal Geographical Society (FRGS), he also serves on editorial boards of several top-tier journals. His research integrates geospatial AI and deep learning for disaster risk reduction, land use planning, and sustainability. Daichao Sheng is a distinguished professor and the head of School of Civil and Environmental Engineering. He has developed an internationally recognized profile in computational geomechanics including soft computing, unsaturated soils, geo-risk analysis and transport geotechnics. He has published 300+ peer-reviewed papers and two books, including 200+ papers in top geotechnical and computational mechanics journals. These publications now attract 1400+ citations per annum, with an H-Index of 48 in Scopus. His track record places him easily within the top handful of geomechanics professionals of his age worldwide. He has collaborated widely with Australian and international researchers in his field Xuzhen He is a senior lecturer at UTS School of Civil and Environmental Engineering. He is an early career researcher and completed his undergraduate and PhD training at the world’s top universities (Tsinghua for his BSc and Cambridge for his PhD) and was awarded the John Winbolt Prize and the Raymond and Helen Kwok Scholarship from Cambridge University. He was awarded the Australian Research Council Discovery Early Career Researcher Award in 2021. His research interest lies mainly in computational geomechanics, and he has published 30+ high-quality journal papers in these areas.

• Part 1: Machine learning methods and connections between different parts. 1. Machine learning methods2. Connections between studies across different scales3. Summary and outlookPart 2: Machine learning in microscopic modelling of geo-materials.4. Machine-learning-enabled discrete element method5. Machine learning in micromechanics based virtual laboratory testing6. Integrating X-ray CT and machine learning for better understanding of granular materials7. Summary and outlookPart 3: Machine learning in constitutive modelling of geo-materials. 8. Thermodynamics-driven deep neural network as constitutive equations9. Deep active learning for constitutive modelling of granular materials10. Summary and outlookPart 4: Machine learning in design of geo-structures. 11. Deep learning for surrogate modelling for geotechnical risk analysis12. Deep learning for geotechnical optimization of designs13. Deep learning for time series forecasting in geotechnical engineering14. Summary and outlookPart 5: Machine learning in geo-risk susceptibility mapping for regions of various sizes. 15. Deep learning and ensemble modeling of debris flows, mud flows and rockfalls.16. Integrating machine learning and physical-based models in landslide susceptibility and hazard mapping.17. Explainable AI (XAI) in landslide susceptibility, hazard, vulnerability and risk assessment.18. New approaches for data collection for susceptibility mapping19. Summary and outlook

Professor Pradhan is a globally recognized expert in geospatial analytics and artificial intelligence applications in Earth and environmental sciences. Currently a Distinguished Professor at the University of Technology Sydney (UTS), Australia, he also leads the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). With a PhD in GIS-based modeling, Prof. Pradhan has over two decades of experience in spatial data science, remote sensing, natural hazard modeling, and environmental monitoring. He has been listed among the world's top 2% scientists by Stanford University and received numerous international awards, including from IEEE and Elsevier. A Fellow of the Royal Geographical Society (FRGS), he also serves on editorial boards of several top-tier journals. His research integrates geospatial AI and deep learning for disaster risk reduction, land use planning, and sustainability. Daichao Sheng is a distinguished professor and the head of School of Civil and Environmental Engineering. He has developed an internationally recognized profile in computational geomechanics including soft computing, unsaturated soils, geo-risk analysis and transport geotechnics. He has published 300+ peer-reviewed papers and two books, including 200+ papers in top geotechnical and computational mechanics journals. These publications now attract 1400+ citations per annum, with an H-Index of 48 in Scopus. His track record places him easily within the top handful of geomechanics professionals of his age worldwide. He has collaborated widely with Australian and international researchers in his field Xuzhen He is a senior lecturer at UTS School of Civil and Environmental Engineering. He is an early career researcher and completed his undergraduate and PhD training at the world’s top universities (Tsinghua for his BSc and Cambridge for his PhD) and was awarded the John Winbolt Prize and the Raymond and Helen Kwok Scholarship from Cambridge University. He was awarded the Australian Research Council Discovery Early Career Researcher Award in 2021. His research interest lies mainly in computational geomechanics, and he has published 30+ high-quality journal papers in these areas.

• Part 1: Machine learning methods and connections between different parts. 1. Machine learning methods2. Connections between studies across different scales3. Summary and outlookPart 2: Machine learning in microscopic modelling of geo-materials.4. Machine-learning-enabled discrete element method5. Machine learning in micromechanics based virtual laboratory testing6. Integrating X-ray CT and machine learning for better understanding of granular materials7. Summary and outlookPart 3: Machine learning in constitutive modelling of geo-materials. 8. Thermodynamics-driven deep neural network as constitutive equations9. Deep active learning for constitutive modelling of granular materials10. Summary and outlookPart 4: Machine learning in design of geo-structures. 11. Deep learning for surrogate modelling for geotechnical risk analysis12. Deep learning for geotechnical optimization of designs13. Deep learning for time series forecasting in geotechnical engineering14. Summary and outlookPart 5: Machine learning in geo-risk susceptibility mapping for regions of various sizes. 15. Deep learning and ensemble modeling of debris flows, mud flows and rockfalls.16. Integrating machine learning and physical-based models in landslide susceptibility and hazard mapping.17. Explainable AI (XAI) in landslide susceptibility, hazard, vulnerability and risk assessment.18. New approaches for data collection for susceptibility mapping19. Summary and outlook