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,…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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
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
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