Buch, Englisch, 67 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1474 g
Buch, Englisch, 67 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1474 g
Reihe: SpringerBriefs in Applied Sciences and Technology
ISBN: 978-981-10-0662-3
Verlag: Springer Nature Singapore
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Energie- & Versorgungswirtschaft Wasserwirtschaft
- Naturwissenschaften Agrarwissenschaften Agrarwissenschaften
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Geowissenschaften Umweltwissenschaften Klimawandel, Globale Erwärmung
Weitere Infos & Material
1. Introduction
1.1 Climate
1.2 Climate Change
1.3 Impact of Climate Change in Global Context
1.4 Impact of Climate Change on Agriculture
1.5 Climatological Parameters Affecting Crop Yeild
1.5 Downscaling1.6 Types of Downscaling
1.7 Multiple Linear Regression
1.8 Principal Component Analysis
1.9 Objectives
2.Principal Component Analysis
2.1 Statistical Downscaling Methods
2.2 Principal Component Analysis
2.2.1 Advantages of PCA
2.2.2 Disadvantages of PCA
2.2.3 Applications of Principal Components Analysis2.3 Principal Component Regression
2.3.1 Calculating Principal Components
2.3.2 Rules for Retaining Principal Components
2.3.3 Development of Principal Component Regression
3. Review of Literature3.1 Review of Works on Climate Change
3.2 Review of Works on Downscaling Techniques
3.3 Review of Works on Multiple Linear Regression
3.4 Review of Works on Principal Component Analysis/Regression
4. Study Area and Data Analysis
4.1 Study Area
4.2 Data Analysis
5. Methodology
5.1 Multiple Linear Regression Model
5.2 Principal Component Regression Model
5.3 Performance Indices5.4 Analysis of MLR and PCR Models
6. Results and Analysis
6.1 MLR Model
6.1.1 MLR During Training
6.1.2 MLR During Validation
6.2 PCR Model
6.2.1 PCR During Training
6.2.2 PCR During Validation
6.3 Comparison of MLR and PCR Models Using Performance Indices
6.4 Analysis of PCR Model
7. Conclusions



