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 Research Objectives 1.3 Scope of the Study 1.4 Structure of the Thesis 2. Literature Review 2.1 Deep Learning in Hydrology 2.2 Streamflow Modelling Challenges 2.3 Data-Scarce Conditions Analysis 2.4 Existing Multi-Criteria Frameworks 3. Theoretical Framework 3.1 Deep Learning Algorithms Overview 3.2 Criteria for Model Evaluation 3.3 Integration of Multiple Data Sources 4. Methodology 4.1 Study Area and Data Collection 4.2 Model Development Process 4.3 Evaluation Techniques 4.4 Limitations and Assumptions 5. Case Study: Streamflow in Eswatini 5.1 Geographic and Climatic Overview 5.2 Hydrological Data Characteristics 5.3 Application of the Framework 5.4 Results Interpretation 6. Results and Discussion 6.1 Model Performance Analysis 6.2 Comparison with Traditional Methods 6.3 Implications for Water Resource Management 6.4 Discussion of Findings 7. Conclusions 7.1 Summary of Key Findings 7.2 Contributions to the Field 7.3 Recommendations for Future Research 8. References and Further Reading 8.1 Primary Sources 8.2 Additional Literature 8.3 Data Repositories and Tools