Computational Methods for Time-Series Analyses in Earth Sciences bridges the gap between theoretical knowledge and practical application, offering a deep dive into the utilization of R programming for managing, analyzing, and forecasting time-series data within the realm of Earth sciences. It systematically unfolds the layers of data manipulation, graphical representation, and sampling to prepare the reader for complex analyses and predictive modeling from the basics of signal processing to the nuances of machine learning. It presents cutting-edge techniques, such as neural networks, kernel-based methods, and evolutionary algorithms, specifically tailored to tackle challenges, and provides practical case studies to aid readers with utilizing the techniques covered.
Computational Methods for Time-Series Analyses in Earth Sciences is a valuable resource for scientists, researchers, and students delving into the intricacies of Earth's environmental patterns and cycles through the lens of computational analysis and guides readers through various computational approaches to deciphering spatial and temporal data.
1. Chapter 1 Introduction to R: Data manipulation, graphics, and sampling - p. 3 2. Chapter 2 Time series analysis in the earth sciences with R - p. 45 3. Chapter 3 Signal processing in the earth sciences with R - p. 111 4. Chapter 4 Spatial analyses with R - p. 147 5. Chapter 5 Deterministic modeling with R - p. 205 6. Abstract - p. 205 7. 5.1 Introduction - p. 205 8. 5.2 Solving equations with R - p. 206 9. 5.3 Solving ODEs - p. 220 10. 5.4 One-dimensional optimization - p. 244 11. 5.5 Multidimensional optimization - p. 247 12. 5.6 Model metrics - p. 262 13. 5.7 Model calibration - p. 267 14. 5.7.1 Equifinality - p. 269 15. 5.8 Introduction to sensitivity analyses - p. 270 16. 5.8.1 Global sensitivity analysis - p. 270 17. 5.8.2 Local sensitivity analysis - p. 271 18. 5.8.3 Monte Carlo filtering - p. 271 19. 5.9 Sensitivity package - p. 272 20. References - p. 276 21. Chapter 6 R for machine learning in Earth sciences - p. 279 22. Section B: Case studies and applications - p. 315 23. Chapter 7 Predicting the soil hydraulic properties and drainage capacities of sandy soils using a neural network - p. 317 24. Chapter 8 Prognostication of real-time hourly precipitation using deep learning techniques - p. 337 25. Chapter 9 Integration of hydrometric upstream runoff and Optimal Pruned Extreme Learning Machine for real-time flood forecasting - p. 371 26. Chapter 10 Prediagnosis of flooding using real-time monitoring of climate parameters - p. 405 27. Chapter 11 Comprehensive comparison of the local and external data analysis approaches for precipitation forecasting - p. 439 28. Chapter 12 An intelligent real-time system for warning of flash floods - p. 479 29. Chapter 13 Beginner-friendly guide to hydroclimate data collection through Google Earth Engine and application development - p. 511 30. Chapter 14 Mining and refining hydroclimatic data for hybrid AI-driven sustainability models - p. 575 31. Chapter 15 Hybrid AI-powered models and cloud-based solutions for hydroclimatic forecasting and sustainable soil and water management—Linear modeling foundation - p. 601 32. Chapter 16 Hybrid AI-Powered Models and Cloud-Based Solutions for Hydroclimatic Forecasting and Sustainable Soil and Water Management – Nonlinear modeling approach - p. 633 33. Chapter 17 A combination of satellite observations and machine learning technique for terrestrial water storage anomaly estimation (case study: St. Lawrence watershed, Quebec, Canada) - p. 663 34. Index - p. 697
Computational Methods for Time-Series Analyses in Earth Sciences bridges the gap between theoretical knowledge and practical application, offering a deep dive into the utilization of R programming for managing, analyzing, and forecasting time-series data within the realm of Earth sciences. It systematically unfolds the layers of data manipulation, graphical representation, and sampling to prepare the reader for complex analyses and predictive modeling from the basics of signal processing to the nuances of machine learning. It presents cutting-edge techniques, such as neural networks, kernel-based methods, and evolutionary algorithms, specifically tailored to tackle challenges, and provides practical case studies to aid readers with utilizing the techniques covered.
Computational Methods for Time-Series Analyses in Earth Sciences is a valuable resource for scientists, researchers, and students delving into the intricacies of Earth's environmental patterns and cycles through the lens of computational analysis and guides readers through various computational approaches to deciphering spatial and temporal data.
1. Chapter 1 Introduction to R: Data manipulation, graphics, and sampling - p. 3 2. Chapter 2 Time series analysis in the earth sciences with R - p. 45 3. Chapter 3 Signal processing in the earth sciences with R - p. 111 4. Chapter 4 Spatial analyses with R - p. 147 5. Chapter 5 Deterministic modeling with R - p. 205 6. Abstract - p. 205 7. 5.1 Introduction - p. 205 8. 5.2 Solving equations with R - p. 206 9. 5.3 Solving ODEs - p. 220 10. 5.4 One-dimensional optimization - p. 244 11. 5.5 Multidimensional optimization - p. 247 12. 5.6 Model metrics - p. 262 13. 5.7 Model calibration - p. 267 14. 5.7.1 Equifinality - p. 269 15. 5.8 Introduction to sensitivity analyses - p. 270 16. 5.8.1 Global sensitivity analysis - p. 270 17. 5.8.2 Local sensitivity analysis - p. 271 18. 5.8.3 Monte Carlo filtering - p. 271 19. 5.9 Sensitivity package - p. 272 20. References - p. 276 21. Chapter 6 R for machine learning in Earth sciences - p. 279 22. Section B: Case studies and applications - p. 315 23. Chapter 7 Predicting the soil hydraulic properties and drainage capacities of sandy soils using a neural network - p. 317 24. Chapter 8 Prognostication of real-time hourly precipitation using deep learning techniques - p. 337 25. Chapter 9 Integration of hydrometric upstream runoff and Optimal Pruned Extreme Learning Machine for real-time flood forecasting - p. 371 26. Chapter 10 Prediagnosis of flooding using real-time monitoring of climate parameters - p. 405 27. Chapter 11 Comprehensive comparison of the local and external data analysis approaches for precipitation forecasting - p. 439 28. Chapter 12 An intelligent real-time system for warning of flash floods - p. 479 29. Chapter 13 Beginner-friendly guide to hydroclimate data collection through Google Earth Engine and application development - p. 511 30. Chapter 14 Mining and refining hydroclimatic data for hybrid AI-driven sustainability models - p. 575 31. Chapter 15 Hybrid AI-powered models and cloud-based solutions for hydroclimatic forecasting and sustainable soil and water management—Linear modeling foundation - p. 601 32. Chapter 16 Hybrid AI-Powered Models and Cloud-Based Solutions for Hydroclimatic Forecasting and Sustainable Soil and Water Management – Nonlinear modeling approach - p. 633 33. Chapter 17 A combination of satellite observations and machine learning technique for terrestrial water storage anomaly estimation (case study: St. Lawrence watershed, Quebec, Canada) - p. 663 34. Index - p. 697
