Computational Methods for Time-Series Analysis in Earth Sciences
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Computational Methods for Time-Series Analysis in Earth Sciences
Gumiere, Silvio Jose; Bonakdari, Hossein
Elsevier - Health Sciences Division
05/2025
420
Mole
9780443336317
Pré-lançamento - envio 15 a 20 dias após a sua edição
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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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Time-series analysis; Earth sciences; Computational methods; Machine learning; Environmental data; R programming; Hydrological forecasting; Neural networks; Spatial analyses; Climate modeling
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
