Computational Methods for Time-Series Analysis in Earth Sciences

Erscheinungsjahr 2025
ISBN: 978-0-443-33631-7
Verlag: Elsevier Science

Buch, Englisch, 420 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 450 g

Computational Methods for Time-Series Analysis 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 Earth sciences. The book 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.

This 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. It guides readers through various computational approaches for deciphering spatial and temporal data.

Autoren/Hrsg.

Gumiere, Silvio José

Bonakdari, Hossein

Fachgebiete

• Geowissenschaften Geologie Geologie
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware

Inhaltsverzeichnis

Section 1: Theory and Computational Methods
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

Über Autor(innen)

Bonakdari, Hossein
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.

Gumiere, Silvio José
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.