Intelligence Systems for Earth, Environmental and Planetary Sciences: Methods, Models and Applications provides cutting-edge theory and applications of modern-day artificial intelligence and data science in the Earth, environment, and planetary science fields. The book is divided into three sections: (i) Methods, covering the fundamentals of intelligence systems, along with an introduction to the preparation of datasets; (ii) Models, detailing model development, data assimilation, and techniques in each field; and (iii) Applications, presenting case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives. Intelligence Systems for Earth, Environmental and Planetary Sciences will be of interest to students, academics, and postgraduate professionals in the field of applied sciences, Earth, environmental, and planetary sciences and would also serve as an excellent companion resource to courses studying artificial intelligence applications for theoretical and practical studies in Earth, environmental, and planetary sciences.
1. Intelligence Systems for Earth, Environmental and Planetary Sciences - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Dedication - p. v 7. Contributors - p. xvii 8. Acknowledgments - p. xxi 9. Chapter 1 Partial least squares regression to explore and predict environmental data - p. 1 10. Chapter 2 Study of solute dynamics in unsaturated sandy soil under controlled irrigation - p. 33 11. Chapter 3 Prediction of hydraulic heights water table for irrigation management in cranberry fields with Random Forest: A tutorial - p. 63 12. Chapter 4 Spatial intelligence in AI applications for assessing soil health to monitor farming systems and associated ESG risk - p. 81 13. Chapter 5 Drought forecasting based on machine learning techniques - p. 113 14. Chapter 6 How to use artificial intelligence to downscale climate change models’ data - p. 147 15. Chapter 7 Advanced methods of soil quality assessment for sustainable agriculture - p. 185 16. Chapter 8 Combining the RUSLE approach and GIS tools in soil water erosion monitoring and mapping (Northeastern Algeria) - p. 209 17. Chapter 9 Leveraging the use of mechanistic and machine learning models to assess interactions between ammonia concentrations, manure characteristics, and atmospheric conditions in laying-hen manure storage under laboratory conditions - p. 229 18. Chapter 10 Fallout radionuclide (137Cs) method for quantifying soil erosion rates in steep sloping hilly and mountainous landscapes of Himalayas - p. 261 19. Chapter 11 Coupling AquaCrop and machine learning approaches for cotton yield simulation - p. 291 20. Chapter 12 Attention-based Deep Neural Network for rainfall-runoff simulation across the continental United States - p. 315 21. Chapter 13 Digital soil mapping using geospatial data and machine learning techniques - p. 335 22. Chapter 14 Application of gene expression programming for prediction of dilution of inclined dense jet after the impact point based on experimental data - p. 379 23. Chapter 15 Evolutionary prediction of geometrical and dilution characteristics of inclined dense jet over a sloped bottom using results from large-eddy simulation - p. 409 24. Chapter 16 Soil temperature prediction in ordinary and extremely hot weather using genetic programming - p. 441 25. Chapter 17 Feasibility of one-dimensional simulation of dam break via a novel finite volume scheme - p. 465 26. Author Index - p. 487 27. Index - p. 515
Intelligence Systems for Earth, Environmental and Planetary Sciences: Methods, Models and Applications provides cutting-edge theory and applications of modern-day artificial intelligence and data science in the Earth, environment, and planetary science fields. The book is divided into three sections: (i) Methods, covering the fundamentals of intelligence systems, along with an introduction to the preparation of datasets; (ii) Models, detailing model development, data assimilation, and techniques in each field; and (iii) Applications, presenting case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives. Intelligence Systems for Earth, Environmental and Planetary Sciences will be of interest to students, academics, and postgraduate professionals in the field of applied sciences, Earth, environmental, and planetary sciences and would also serve as an excellent companion resource to courses studying artificial intelligence applications for theoretical and practical studies in Earth, environmental, and planetary sciences.
1. Intelligence Systems for Earth, Environmental and Planetary Sciences - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Dedication - p. v 7. Contributors - p. xvii 8. Acknowledgments - p. xxi 9. Chapter 1 Partial least squares regression to explore and predict environmental data - p. 1 10. Chapter 2 Study of solute dynamics in unsaturated sandy soil under controlled irrigation - p. 33 11. Chapter 3 Prediction of hydraulic heights water table for irrigation management in cranberry fields with Random Forest: A tutorial - p. 63 12. Chapter 4 Spatial intelligence in AI applications for assessing soil health to monitor farming systems and associated ESG risk - p. 81 13. Chapter 5 Drought forecasting based on machine learning techniques - p. 113 14. Chapter 6 How to use artificial intelligence to downscale climate change models’ data - p. 147 15. Chapter 7 Advanced methods of soil quality assessment for sustainable agriculture - p. 185 16. Chapter 8 Combining the RUSLE approach and GIS tools in soil water erosion monitoring and mapping (Northeastern Algeria) - p. 209 17. Chapter 9 Leveraging the use of mechanistic and machine learning models to assess interactions between ammonia concentrations, manure characteristics, and atmospheric conditions in laying-hen manure storage under laboratory conditions - p. 229 18. Chapter 10 Fallout radionuclide (137Cs) method for quantifying soil erosion rates in steep sloping hilly and mountainous landscapes of Himalayas - p. 261 19. Chapter 11 Coupling AquaCrop and machine learning approaches for cotton yield simulation - p. 291 20. Chapter 12 Attention-based Deep Neural Network for rainfall-runoff simulation across the continental United States - p. 315 21. Chapter 13 Digital soil mapping using geospatial data and machine learning techniques - p. 335 22. Chapter 14 Application of gene expression programming for prediction of dilution of inclined dense jet after the impact point based on experimental data - p. 379 23. Chapter 15 Evolutionary prediction of geometrical and dilution characteristics of inclined dense jet over a sloped bottom using results from large-eddy simulation - p. 409 24. Chapter 16 Soil temperature prediction in ordinary and extremely hot weather using genetic programming - p. 441 25. Chapter 17 Feasibility of one-dimensional simulation of dam break via a novel finite volume scheme - p. 465 26. Author Index - p. 487 27. Index - p. 515
