Yellow River Conservancy Technical Institute

<strong class="journal-contentHeaderColor">Abstract.</strong> Reliable urban flood prediction hinges on how datasets are designed, yet most existing research disproportionately emphasizes network architectures over data foundations. This study systematically investigates how dataset characteristics&mdash;scale, feature composition, and rainfall-event distribution&mdash;govern predictive performance and generalization in AI-based flood modeling. A physically calibrated hydrological&ndash;hydrodynamic model was employed to generate synthetic datasets with varied temporal lengths, input feature combinations (rainfall, infiltration, drainage), and rainfall-intensity distributions. A long short-term memory (LSTM) network, chosen for its widespread adoption and proven performance in hydrology, was applied as a representative benchmark to assess accuracy, computational cost, and bias under controlled conditions. Results identify: (1) a threshold effect of dataset length (~14,400 samples), beyond which performance gains plateau; (2) rainfall-intensity distribution as the dominant driver of generalization&mdash;training solely on light or extreme events induces systematic bias, whereas mixed-intensity datasets substantially enhance robustness; (3) ancillary features (infiltration and drainage) improve stability only when data are sufficiently abundant. These findings quantify trade-offs and pinpoint actionable design levers, offering general insights into dataset design for machine learning models in flood prediction and beyond. By clarifying critical dataset requirements, this study provides transferable guidance for building efficient and balanced datasets in hydrology and broader Earth system sciences.

ABSTRACT Flash floods in mountainous regions pose significant threats, yet comparative studies of different modeling approaches under consistent conditions remain limited. This study systematically compares four hydrological modeling approaches representing distinct philosophies for flash flood forecasting across 12 mountainous watersheds in China: parameter estimation-based flash flood model, storage-infiltration compatible model, Hydrologic Engineering Center's Hydrologic Modeling System, and long short-term memory neural network. Using 236 historical flood events with a 70%/30% calibration–validation framework, we evaluate model performance through prediction accuracy, temporal precision, and computational characteristics. Results reveal distinct performance patterns: Hydrologic Engineering Center's Hydrologic Modeling System achieved highest Nash–Sutcliffe efficiency (0.783), followed by storage-infiltration compatible (0.782), long short-term memory (0.745), and parameter estimation-based flash flood model (0.740). For peak flow prediction, parameter estimation-based flash flood model demonstrated highest qualification rates (87.03%), followed by long short-term memory (84.04%), storage-infiltration compatible (79.6%), and Hydrologic Engineering Center's Hydrologic Modeling System (79.12%). Peak timing accuracy showed comparable performance across models (0.89–1.04 h: average error).The comparative analysis reveals model-specific strengths: physically-based models excel in overall hydrograph simulation, conceptual models provide balanced performance, data-driven approaches show efficient pattern recognition, while parameter estimation methods demonstrate advantages in peak flow prediction. Study provides objective benchmarks for flash flood forecasting.

Abstract The scarcity of detailed drainage network data severely constrains urban flood modeling and risk assessment. To address this challenge, this study proposes a novel Dynamic Fusion Method (DFM) for equivalent drainage modeling in data-scarce areas. The DFM dynamically integrates three existing approaches—the Rainfall Reduction Method (RRM), Road-based Equivalent Drainage Method (REDM), and Stormwater Inlet Equivalent Drainage Method (SIEDM)—using a rainfall-adaptive nonlinear weighting function. A high-resolution 1D/2D coupled model (SWMM/HEC-RAS), validated against historical inundation records, was established as a benchmark (HRPN) to evaluate the DFM against individual methods under various design storm scenarios in a typical urbanized catchment in Nanjing, China. The comparative results reveal a critical trade-off between volumetric error and spatial reliability. While the RRM produced the lowest total area error, it suffered from significant under-prediction, failing to identify critical flood-prone zones. In contrast, the DFM demonstrated superior spatial consistency, achieving the highest Intersection over Union (IoU) with the benchmark (average IoU of 0.268), outperforming RRM and SIEDM by 16.0% and 10.7%, respectively. Mechanistically, the DFM’s adaptive weighting system effectively acts as a proxy for the drainage system's nonlinear state transition, shifting dominance from global capacity reduction during light rain to localized, surface-based drainage representation during extreme peaks. Although the DFM tends toward a conservative over-prediction of inundated areas, it avoids the dangerous underestimation risks associated with traditional static methods. These findings suggest that the DFM provides a robust and safer alternative framework for high-precision flood risk banding and management in regions lacking detailed infrastructure data.

To evaluate and ensure the safety of a prefabricated connection scheme for future engineering applications, this study investigates the life-cycle progressive collapse resistance of prefabricated reinforced concrete frame structures (PRCS) with steel tube stud connections. A simplified prefabricated column model was developed in SAP2000 using multi-segment linear plastic connection elements, and a six-story PRCS was established for analysis. Progressive collapse behavior of the PRCS and a comparable cast-in-place concrete frame structure (CPCS) was evaluated through the column removal method under four scenarios: corner column, long-side middle column, short-side middle column, and interior column removal. Time-history responses of internal forces and displacements were obtained. In addition, the effect of chloride-induced corrosion on the collapse performance of the structure was analyzed through pushdown analyses. The results indicate that the proposed connection model accurately reproduces the behavior of prefabricated column joints. Among the four scenarios, interior column removal had the most severe effect on PRCS, with displacement increasing by 72.2% compared to CPCS. Pushdown analysis revealed that PRCS exhibited 25.4% lower beam mechanism capacity when the corner column was removed, and 33.1% lower catenary mechanism capacity when the interior column was removed. Furthermore, long-term corrosion significantly reduced the progressive collapse capacity, underscoring the necessity of considering durability effects in design.

<title>Abstract</title> This study focuses on the nine provinces along the Yellow River, and establishes a water resources system resilience evaluation framework consisting of 34 indicators based on a meteorology-hydrology-socioeconomy-ecology-engineering multidimensional system. By applying a TOPSIS for assessing water resources system resilience, that incorporates combination weighting approach based on game theory, this study investigates the spatiotemporal evolution of the water resources system resilience from 2009 to 2022.The resilience of water resources system in the provinces along the Yellow River exhibited an overall fluctuating upward trend. Since 2019, resilience levels generally increased, with the lowest values of 0.36 in 2009 and 2010, and the highest value of 0.59 in 2021. Some provinces, including Shanxi, Gansu, and Qinghai experienced significant fluctuations in resilience due to climate variability and the implementation of local policies, whereas regions such as Shaanxi and Shandong maintained relatively stable resilience levels. From 2009 to 2022, the resilience levels of water resources system in the provinces along the Yellow River were ranked in descending order: Sichuan &gt; Henan &gt; Shaanxi &gt; Inner Mongolia &gt; Qinghai &gt; Shandong &gt; Ningxia &gt; Gansu &gt; Shanxi. Sichuan and Henan achieved Level II (higher resilience), with the rest at Level III (moderate resilience)

<title>Abstract</title> As a synthetic material, cemented sand and gravel (CSG) holds significant promise for application in dam construction. However, research on the mechanical properties and constitutive models of CSG remains limited. This study establishes a binary medium constitutive model for CSG based on triaxial test data obtained under varying gel content and confining pressure conditions, as well as the theoretical framework of continuum mechanics. The triaxial test results indicate that the stress-strain curve of CSG exhibits strain softening behavior. With increasing confining pressure, the volumetric strain curve transitions from initial volumetric shrinkage followed by volumetric dilatancy to complete volumetric shrinkage. The strain softening and dilatancy characteristics of CSG become more pronounced with higher gel content. In the proposed constitutive model, CSG are represented as a composite of a bonded element and a frictional element. The bonded element demonstrates linear elastic behavior, while the frictional element exhibits elastoplastic properties. Under external loading, the bonded element progressively degrades into the frictional element, with both elements sharing the load. To describe the interaction between the bonded and frictional elements within the representative volume element (RVE), the homogenization theory introduces failure rate and strain concentration coefficients, thereby capturing the evolution of internal structure and non-uniform strain distribution during loading. Comparisons between experimental and simulated curves confirm that the model accurately predicts the deformation characteristics of CSG, validating the rationality of the constitutive model.

The skid resistance of tunnel cement concrete pavement is directly related to driving safety in tunnels, and restoring skid resistance is a common issue faced in the operation of tunnel cement concrete pavement. Based on engineering cases, this article introduces the key technical parameters, process flow, and treatment effects of three skid resistance restoration techniques for the skid resistance treatment of operating tunnel cement concrete pavement, namely milling and grooving, HOG surface texturing technology, and bauxite skid resistance surface layer. The aim is to provide a reference for other skid resistance treatments of tunnel cement concrete pavement.