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Intelligence Systems for Earth, Environmental and Planetary Sciences
Methods, Models and Applications
- 1st Edition - July 30, 2024
- Editors: Hossein Bonakdari, Silvio José Gumiere
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 2 9 3 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 2 9 2 - 6
Intelligence Systems for Earth, Environmental and Planetary Sciences: Methods, Models and Applications provides cutting-edge theory and applications of modern-day artificia… Read more
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Request a sales quoteIntelligence 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.
- Facilitates the application of artificial intelligence and data science systems to create comprehensive methodologies for analyzing, processing, predicting, and management strategies in the fields of Earth, environment, and planetary science
- Developed with an interdisciplinary framework, with an aim to promote artificial intelligence models for real-time Earth systems
- Includes a section on case studies of artificial intelligence and data science solutions to Earth, environmental, and planetary sciences problems, as well as future perspectives
Graduate students and post-graduate professionals in the field of applied sciences, earth, environmental and planetary sciences
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Contributors
- Acknowledgments
- Chapter 1 Partial least squares regression to explore and predict environmental data
- Abstract
- Acknowledgment
- 1 Introduction
- 2 PLSR case study: A step-by-step illustration of applying PLSR to environmental data using R
- 3 Key practical considerations for PLS applications
- 4 Future perspectives with PLSR
- References
- Chapter 2 Study of solute dynamics in unsaturated sandy soil under controlled irrigation
- Abstract
- 1 Introduction
- 2 Materials and method
- 3 Results and discussion
- 4 Conclusion
- Appendix
- References
- Chapter 3 Prediction of hydraulic heights water table for irrigation management in cranberry fields with Random Forest: A tutorial
- Abstract
- 1 Introduction
- 2 Methodology
- 3 Results and discussion
- 4 Conclusion
- References
- Chapter 4 Spatial intelligence in AI applications for assessing soil health to monitor farming systems and associated ESG risk
- Abstract
- 1 Introduction
- 2 Data and methodology
- 3 Results
- 4 Discussion
- Conclusion
- Funding
- Author contributions
- Declaration of competing interest
- References
- Chapter 5 Drought forecasting based on machine learning techniques
- Abstract
- 1 Introduction
- 2 Background
- 3 Frequency, severity, and areal extend of drought
- 4 Return period and indexes of drought
- 5 Conclusion
- References
- Chapter 6 How to use artificial intelligence to downscale climate change models’ data
- Abstract
- 1 Introduction
- 2 Background
- 3 The coupled model intercomparison project
- 4 How to use
- 5 Conclusion
- References
- Chapter 7 Advanced methods of soil quality assessment for sustainable agriculture
- Abstract
- 1 Introduction
- 2 The concepts and applications of soil quality
- 3 Soil quality datasets
- 4 Soil quality assessment methods
- 5 Radio isotope technique to assess soil quality
- 6 Conclusion
- References
- Chapter 8 Combining the RUSLE approach and GIS tools in soil water erosion monitoring and mapping (Northeastern Algeria)
- Abstract
- 1 Introduction
- 2 Location and geological setting
- 3 Materials and methods
- 4 Results and discussion
- 5 Conclusions
- References
- 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
- Abstract
- 1 Introduction
- 2 Materials and methods
- 3 Results
- 4 Discussion
- 5 Conclusions
- Appendix A: Carbon-dioxide accelerated ammonia release model
- References
- Chapter 10 Fallout radionuclide (137Cs) method for quantifying soil erosion rates in steep sloping hilly and mountainous landscapes of Himalayas
- Abstract
- 1 Soil erosion—A major land degradation concerns in hilly and mountainous landscapes
- 2 RS and GIS for characterizing hilly and mountainous landscape
- 3 Soil erosion assessment (measurement and modeling)
- 4 Fallout radionuclides/radioisotopes for soil erosion measurement
- 5 Advances in geospatial methods for erosion estimation
- 6 FRN-based soil erosion estimation in a steep hillslope of Himalayas—Is it possible?
- 7 Conclusion
- References
- Chapter 11 Coupling AquaCrop and machine learning approaches for cotton yield simulation
- Abstract
- Acknowledgment
- 1 Introduction
- 2 Materials and methods
- 3 Results and discussion
- 4 Conclusion
- References
- Chapter 12 Attention-based Deep Neural Network for rainfall-runoff simulation across the continental United States
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Methodology
- 3 Results and discussion
- 4 Conclusion
- References
- Chapter 13 Digital soil mapping using geospatial data and machine learning techniques
- Abstract
- 1 Introduction
- 2 Geospatial data
- 3 Soil–landscape analysis for soil mapping
- 4 Digital soil mapping
- 5 Environmental covariates for DSM
- 6 Proximal sensing and spectroscopy for soil data
- 7 DSM techniques (predictive modeling)
- 8 Accuracy assessment
- 9 Conclusions
- References
- Chapter 14 Application of gene expression programming for prediction of dilution of inclined dense jet after the impact point based on experimental data
- Abstract
- 1 Introduction
- 2 Dimensionless analysis for inclined dense jet
- 3 Methodology
- 4 Results and discussion
- 5 Conclusion
- Appendix A
- Appendix B
- References
- Chapter 15 Evolutionary prediction of geometrical and dilution characteristics of inclined dense jet over a sloped bottom using results from large-eddy simulation
- Abstract
- 1 Introduction
- 2 Methodology
- 3 Results and discussion
- 4 Conclusion
- Appendix: The models produced by single-gene and three-gene GEP models
- References
- Chapter 16 Soil temperature prediction in ordinary and extremely hot weather using genetic programming
- Abstract
- Acknowledgment
- 1 Introduction
- 2 Materials and methods
- 3 Results and discussion
- 4 Conclusions
- Appendix A
- References
- Chapter 17 Feasibility of one-dimensional simulation of dam break via a novel finite volume scheme
- Abstract
- 1 Introduction
- 2 Methodology
- 3 Results and discussion
- 4 Conclusions
- References
- Author Index
- Index
- No. of pages: 290
- Language: English
- Edition: 1
- Published: July 30, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780443132933
- eBook ISBN: 9780443132926
HB
Hossein Bonakdari
Prof. Hossein Bonakdari obtained his PhD in civil engineering from the University of Caen Normandy, France. He has worked for several organizations, most recently as Professor at the Department of Civil Engineering, University of Ottawa, Canada. He is one of the most influential scientists in the field of developing novel algorithms for solving practical problems through the decision-making abilities of artificial intelligence. His research also focuses on creating comprehensive methodologies in the areas of simulation modeling, optimization, and machine learning algorithms. The results obtained from his research have been published in international journals and presented at international conferences. He was included in the list of the world’s top 2% scientists, published by Stanford University, and is on the editorial board of several journals.
Affiliations and expertise
Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Ottawa, Ottawa, Ontario, CanadaSG
Silvio José Gumiere
Prof. Silvio José Gumiere has been Professor at the Department of Soils and Agri-Food Engineering, Laval University, Canada, since 2011. He is an expert on the application of R-based numerical, statistical, and geostatistical methods, such as time series analyses, image and signal processing, erosion modeling, spatial hydrology, and spatial interpolation methods. His research has been published in international journals and conferences. He is an editor for several journals on hydrological modeling and machine learning techniques for solving applied science problems in hydrology, soil sciences, soil hydrology, and environmental journals.
Affiliations and expertise
Department of Soil Sciences, Laval University, Quebec City, CanadaRead Intelligence Systems for Earth, Environmental and Planetary Sciences on ScienceDirect