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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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.

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

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Intelligence Systems for Earth, Environmental and Planetary Sciences

Methods, Models and Applications

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Hossein Bonakdari

Silvio José Gumiere