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Computational Methods for Time-Series Analysis in Earth Sciences
- 1st Edition - January 1, 2025
- Authors: Silvio José Gumiere, Hossein Bonakdari
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 3 6 3 1 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 3 6 3 2 - 4
Computational Methods for Time-Series Analysis 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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Request a sales quoteComputational 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.
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.
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
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
- No. of pages: 420
- Language: English
- Edition: 1
- Published: January 1, 2025
- Imprint: Elsevier
- Paperback ISBN: 9780443336317
- eBook ISBN: 9780443336324
SG
Silvio José Gumiere
Silvio José Gumiere has been a professor in the Department of Soil Sciences at Laval University since 2011, after obtaining his Ph.D. in hydrological and erosion modelling from the GAIA doctoral School, Montpellier France. Since 2006 he has been working on various aspects of soil erosion, hydrology and soil physics as well as on the development of modelling tools for water and soil management in agricultural systems. 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 modelling and spatial hydrology and spatial interpolation methods. His research has been published in over 50 papers in international journals with over 870 citations. He has also given ~70 presentations at national and international conferences. He is a Guest Editor for several special issues on hydrological modelling and machine learning techniques for solving applied science problems in hydrology, soil sciences, soil hydrology and environmental journals.
Affiliations and expertise
Department of Soil Sciences, Laval University, Quebec City, CanadaHB
Hossein Bonakdari
Dr. Bonakdari obtained his PhD in Civil Engineering from the University of Caen Normandy (France). He has worked for several organizations and most recently as an Associate Professor at the Department of Civil Engineering of the University of Ottawa (Canada). He is one of the most influential scientists in the field of developing novel algorithms for solving practical problems through the decision-making abilities of AI. His research also focuses on creating comprehensive methodologies in the areas of simulation modeling, optimization, and machine learning algorithms. The results obtained from his research have been published in international journals and presented at international conferences. He was included in the list of the world's top 2% scientists, published by Stanford University, and is on the Editorial board of several journals.
Affiliations and expertise
Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Ottawa, Ottawa, Ontario, Canada