Silvio José Gumiere, Hossein Bonakdari

Computational Methods for Time-Series Analyses in Earth Sciences (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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. - 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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 420
• Erscheinungstermin: 27. Juni 2025
• Englisch
• ISBN-13: 9780443336324
• Artikelnr.: 74623395

Autorenporträt

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.

Inhaltsangabe

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