Development of a Multi-Criteria Deep Learning Framework for Streamflow Modelling under Data-Scarce Conditions in Eswatini

1. Introduction 1.1. Background and Motivation 1.2. Objectives of the Study 1.3. Scope and Limitations 1.4. Structure of the Thesis 2. Literature Review 2.1. Overview of Streamflow Modelling 2.2. Deep Learning Techniques 2.3. Multi-Criteria Decision Analysis 2.4. Data-Scarce Environments 2.5. Case Studies in Eswatini 3. Methodology 3.1. Research Design 3.2. Data Collection Techniques 3.3. Framework Development 3.4. Model Training and Testing 3.5. Evaluation Metrics 4. Framework Design 4.1. Architecture Overview 4.2. Integration of Multi-Criteria Analysis 4.3. Handling Data Scarcity 4.4. Algorithm Selection 4.5. Software and Tools Used 5. Data Analysis 5.1. Data Preprocessing Techniques 5.2. Feature Selection and Engineering 5.3. Exploratory Data Analysis 5.4. Challenges and Solutions 5.5. Data Quality Assessment 6. Results and Discussion 6.1. Model Performance Analysis 6.2. Comparison with Other Methods 6.3. Impact of Multi-Criteria Approach 6.4. Sensitivity Analysis 6.5. Practical Implications for Eswatini 7. Case Study: Eswatini 7.1. Geographic and Climatic Context 7.2. Application of Framework 7.3. Key Findings and Insights 7.4. Lessons Learned 7.5. Recommendations for Implementation 8. Conclusion and Future Work 8.1. Summary of Key Findings 8.2. Contributions to the Field 8.3. Limitations of the Study 8.4. Suggestions for Future Research 8.5. Final Remarks