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Computational Methods for Time-Series Analysis in Earth Sciences
◆JpGU2025 セール開催中！：2025年6月30日(月)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 未刊 ご予約承ります。 Author: Gumiere, Silvio Jose/ Bonakdari, Hossein Publisher: Elsevier ISBN: 9780443336317 Cover: PAPERBACK Date: 2025年06月 DESCRIPTION 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. TABLE OF CONTENTS 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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