While 1D, 2D, and coupled 1D/2D models are widely used in flood extent mapping, a significant research gap remains in comparative analyses of 2D and coupled 1D/2D approaches. Study of the Dinsiz Stream Basin is of critical importance due to its proximity to industrial zones and residential areas, as well as its susceptibility to flood risk. Due to the lack and insufficiency of flow data in the basin, only long-term rainfall data were used in the analysis. Rainfall return periods of 50, 100, 200, and 500 years were estimated using statistical methods, and these values were utilized to generate flood hydrographs for this study. These values were then transferred to HEC-HMS, and the resulting hydrographs were input into HEC-RAS to establish coupled 1D/2D and 2D models for comparison. Flood mapping was performed for different return periods to evaluate the flood impact. This study revealed that maximum water levels in the 1D/2D models were higher than in the 2D models. The results showed that Dinsiz Stream could cause major losses for the second organized industrial zone located nearby when it overflows. The accuracy of the model was ensured with photographs of the flood event that occurred in 2021, ensuring the reliability of the findings.

Estimation of sediment concentrations in streams and rivers is important for water resources management and projects. Sediment concentration is generally determined from direct measurements, or estimated from sediment transport equations that require detailed information about the flow and sediment characteristics. However, there is often a large discrepancy between these models and observations. The complexity of sediment transport processes presents an opportunity for the application of alternate methods. As a fairly recent computing tool, relevance vector machines (RVMs) are gaining popularity in the fields of machine learning and pattern recognition. The objective of this study is to develop an RVM approach for estimation of sediment concentrations. The resulting RVM model is then trained and tested on a large data set and the performance of the RVM approach is compared with more conventional transport formulae.

Clustering is necessary for lack of data in a basin based on hydrometeorological homogeneity. Even principal characteristics of river basins, such as; climate, geology, and topography affecting water yields are different, some of them yield similar hydrologic outcome. In this study, 1410 stations of Turkey Rivers were classified by the cluster analysis on the basis of hydrological homogeneity. Monthly average yields (m³/s/km²) of 1410 river gauge stations on 26 river watersheds were used. It is aimed that the clusters to be homogeneous, the elements of the same cluster to be similar while they are not similar to those of a different cluster and the most meaningful groups to be made. The cluster number was found by using the agglomerative hierarchical cluster analysis method. Tests were conducted that stations from different geographic locations are considered in the same cluster independent of their geographic position. Turkey river basins were separated into 6 homogeneous regions and the yield distribution map of Turkey was obtained.

Floods pose significant risks worldwide, impacting lives, infrastructure, and economies. The Susurluk basin, covering 24,319 km² in Türkiye, is highly vulnerable to flooding. This study updates the flood management plan for the basin, integrating hydrological modeling, GIS-based flood mapping, and early warning system evaluations in alignment with the EU Flood Directive. A total of 503 hydrodynamic models (226 one-dimensional and 277 two-dimensional) were developed, analyzing 2116 km of stream length. As a result of the evaluation, the capacities of only 33 streams were found to be sufficient. Flood hazard and risk maps for the Q₅₀, Q₁₀₀, Q₅₀₀, and Q₁₀₀₀ return periods identified the remaining 470 high-risk locations as requiring urgent intervention. Economic risk assessments revealed significant exposure of critical infrastructure, especially in urban areas with populations over 100,000. Furthermore, the study introduces a prioritization framework for intervention that balances socioeconomic costs and environmental impacts. Economic damage assessments estimate potential losses in critical infrastructure, including residential areas, industrial zones, and transportation networks. The findings highlight the importance of proactive flood risk mitigation strategies, particularly in high-risk urban centers. Overall, this study provides a data-driven, replicable model for flood risk management, emphasizing early warning systems, spatial analysis, and structural/non-structural mitigation measures. The insights gained from this research can guide policymakers and urban planners in developing adaptive, long-term flood management strategies for flood-prone regions.