Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya

Inhaltsverzeichnis (Table of Contents)

• CHAPTER ONE: INTRODUCTION
• 1.1 Background
• 1.2 Problem Statement
• 1.3 Research Objectives
• 1.4 Research Questions
• 1.5 Justification of the Study
• 1.6 Scope of the Study
• 1.7 Limitations of the Study
• 1.8 Thesis Organization
• CHAPTER TWO: LITERATURE REVIEW
• 2.1 Drought and its Impacts
• 2.2 Drought Indices
• 2.3 Artificial Neural Networks
• 2.4 Drought Forecasting
• 2.5 Spatial and Temporal Characterization of Drought
• 2.6 Drought Monitoring and Assessment
• 2.7 Applications of Drought Indices
• CHAPTER THREE: MATERIALS AND METHODS
• 3.1 Study Area
• 3.2 Data Sources and Collection
• 3.3 Data Preprocessing and Analysis
• 3.4 Drought Indices Computation
• 3.5 Artificial Neural Network Model Development
• 3.6 Model Training, Validation and Evaluation
• 3.7 Drought Characterization and Forecasting
• CHAPTER FOUR: RESULTS AND DISCUSSION
• 4.1 Spatial and Temporal Drought Characterization
• 4.2 Drought Forecasting Results
• 4.3 Model Evaluation and Performance
• 4.4 Discussion
• CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This thesis aims to investigate the spatio-temporal characterization and forecasting of drought in the Upper Tana River Basin, Kenya, using drought indices and artificial neural networks.

• Drought characterization and forecasting in the Upper Tana River Basin
• Application of drought indices for monitoring and assessment
• Development and evaluation of artificial neural network models for drought forecasting
• Spatial and temporal patterns of drought occurrence and severity
• Contribution to improved drought management and mitigation strategies in the region

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1 introduces the research topic, outlines the problem statement, research objectives, and scope of the study. It also highlights the importance of drought research in the Upper Tana River Basin. Chapter 2 provides a comprehensive review of existing literature related to drought, drought indices, artificial neural networks, and drought forecasting methods. It discusses the strengths and limitations of different approaches and identifies research gaps. Chapter 3 details the materials and methods used in the study, including the study area, data sources, data preprocessing techniques, drought index calculations, artificial neural network model development, and evaluation procedures.

Schlüsselwörter (Keywords)

Drought, drought indices, artificial neural networks, forecasting, spatio-temporal characterization, Upper Tana River Basin, Kenya.