Computational Methods for Time-Series Analyses in Earth Sciences
- 1st Edition - June 20, 2025
- Latest edition
- Authors: Silvio José Gumiere, Hossein Bonakdari
- Language: English
Computational Methods for Time-Series Analyses in Earth Sciences bridges the gap between theoretical knowledge and practical application, offering a deep dive into the utiliz… Read more
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Description
Description
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.
Key features
Key features
- Focuses on the use of R for time-series analysis and the application of these methods directly to Earth and environmental datasets
- Integrates Machine Learning techniques, enabling readers to explore advanced computational methods for forecasting and modeling
- Includes case studies with real-world applications, providing readers with examples on how to translate computational skills into tangible outcomes
Readership
Readership
Table of contents
Table of contents
1. Introduction to R: Data manipulation, graphics, and sampling
2. Time series analysis for earth sciences with R
3. Signal processing with R for earth sciences.
4. Spatial Analyses with R for earth sciences
5. Deterministic modelling with R for earth sciences
6. Machine learning with R for earth sciences
Section 2: Case of Studies and Applications
7. Predicting Sandy Soils' Hydraulic Properties and Drainage Capacities with Neural Networks
8. Prognostication of Real-Time Hourly Precipitation using Kernel-based Techniques
9. Integrating Upstream Runoff and Local Rainfall for Real-Time Flood Prediction
10. Pre-diagnosis of Flooding Using Real-Time Monitoring of Climate Parameters
11. Comparing Local vs. External Data Analysis for Forecasting
12. Evolutionary Kernel Extreme Learning Machine for Real-Time Forecasting
13. A Stochastic AI Method for Predicting Climatic Variables' Spatio-Temporal Changes Under Future Climates – Data Preparation and Preprocessing
14. A Novel AI Stochastic Approach for Predicting Spatio-Temporal Variables and Changes Under Future Climate Conditions: Google Earth Engine's Benefits and Challenges; An Intro to SOILPARAM APP
15. A Novel AI Stochastic Method for Predicting Changes in Space and Time: Linear Modeling
16. A Novel AI Stochastic Method for Predicting Changes: Nonlinear Modeling
17. A Combination of Satellite Observations and Machine Learning Technique for Terrestrial Anomaly Estimation
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 20, 2025
- Language: English
About the authors
About the authors
SG
Silvio José Gumiere
HB
Hossein Bonakdari
Dr. Hossein Bonakdari is a distinguished professor in the Department of Civil Engineering at the University of Ottawa, specializing in mathematical modeling and artificial intelligence (AI). A leading expert in AI-driven data analysis, he has pioneered advanced algorithms for real-time forecasting and big data interpretation, significantly improving the understanding and management of environmental systems.
Dr. Bonakdari has authored four books, published over 320 peer-reviewed journal articles, contributed to more than 20 book chapters, and delivered over 100 presentations at national and international conferences. As a respected editorial board member of several leading journals, he continues to shape research in his field. His groundbreaking contributions have earned him global recognition, ranking him among the top 2% of the world's scientists from 2019 to 2024.