Hasil untuk "River protective works. Regulation. Flood control"

Bambang Agus Kironoto, Miskar Maini, Adam Pamudji Rahardjo et al.

Sediment transport in open channels plays a significant role in shaping turbulent flow structures, influencing sediment dynamics and flow resistance. Transport regimes are classified into equilibrium, where sediment inflow and outflow are balanced, and nonequilibrium, characterized by bed degradation. This study experimentally investigated the turbulence characteristics of sediment-laden, low-velocity open-channel flows under two conditions: sediment-feeding (SF) flows representing equilibrium and nonsediment-feeding (NSF) flows representing degradation-type nonequilibrium conditions. Laboratory experiments were conducted in a 10-m recirculating flume using a 16-MHz acoustic Doppler velocimeter (ADV). Velocity and turbulence profiles were collected under fixed- and movable-bed configurations using two sediment types (d50 = 1.55 and 1.85 mm) simulating tropical riverbeds. Analyses of velocity profiles, turbulence intensities, Reynolds shear stress, mixing length, eddy viscosity, energy spectra, velocity correlations, and turbulence scales were performed. The results reveal clear distinctions between the SF and NSF flows, particularly near the bed. Sediment feeding reduces the near-bed velocity gradient (du/dy), suppresses near-wall turbulence, and shifts the turbulence intensity peak upward to y/H ≈ 0.15. It also significantly reduces the Reynolds shear stress, whereas changes in the eddy viscosity near the bed are less pronounced because of the dominant velocity gradients. A hybrid model combining exponential and power-law terms is proposed to better represent the turbulence intensity and shear stress profiles under sediment-feeding conditions. Spectral analysis confirmed that, despite the 50 Hz sampling limit of the ADV, the inertial subrange follows Kolmogorov's −5/3 law, although the dissipation range was not captured, and microscale estimations remain approximate. Compared with sediment feeding, increased bed roughness reduces turbulence scales, whereas bed mobility effects are secondary. Shear velocity estimates derived from the Clauser, energy gradient, and Reynolds shear stress methods indicate that turbulence-based methods yield more consistent results in sediment-laden flows. These findings advance the understanding of sediment–turbulence interactions and improve sediment transport modeling for low-velocity open channels. Furthermore, these insights can be applied to enhance predictive modeling, optimize sediment management strategies, and support the design of more resilient river engineering structures, particularly in tropical systems.

Wenguang Luo, Yan Pan, Jing Lu et al.

Understanding wind-induced sediment resuspension is essential for predicting turbidity dynamics and nutrient cycling in shallow lakes. This study investigates the spatial variability of sediment resuspension under different hydrodynamic conditions and quantifies the influence of wind-driven forces on sediment stability. A controlled laboratory experiment was conducted using a wind-generation system comprising 13 rows of fans positioned at varying distances and angles with respect to three distinct regions (A, B, and C). Turbidity variations exhibited a strong linear correlation with the dimensionless parameter (W2/H) (R2 = 0.85–0.92), where W represents wind frequency (Hz) and H denotes water depth (m). This parameter effectively captures resuspension sensitivity. Further analysis showed that W, which reflects the proximity to the wind source, integrates the effects of both wind angle and position. Using the 50 NTU water quality threshold, critical (W2/H) values were determined as 2787, 7176, and 16,771 for regions A, B, and C, respectively—corresponding to wind frequencies of 17 Hz, 27 Hz, and 41 Hz at a depth of 0.1 m. Accordingly, regions B and C require approximately 1.6 and 2.5 times more wind energy than region A to reach the same turbidity level. These findings establish a quantitative relationship between wind-driven turbulence and sediment transport, providing insight into the spatial heterogeneity of sediment stability. This research offers both theoretical and practical implications for water quality management, including optimizing artificial aeration, mitigating eutrophication, and improving sediment regulation strategies in shallow lake ecosystems.

Haijing Gao, Qian Wang, Zheng Zhou et al.

The effective management of river networks in coastal plains is crucial to flood control, water quality improvement, and sustainable flow distribution. This study aims to optimize the hydrodynamic performance of a plain river network in eastern China through water diversion and circulation scheduling, addressing challenges such as channel narrowing and sedimentation. This research study utilized a partitioned water allocation approach modeled in MIKE11 to simulate the effects of various diversion projects, including locks and connecting rivers, on the primary conveyance channel and supporting rivers. The simulation results indicated that flow velocities exceeded 0.1 m/s in most rivers, with significant improvements in flood discharge and water quality in the main conveyance channel and one supporting river. However, some sections of the network showed poor hydrodynamic performance due to narrow channels, encroachment, and sedimentation, and smaller rivers exhibited inadequate flow capacity. The findings provide critical insights for optimizing hydrodynamic regulation in coastal plain river systems, emphasizing the need to address specific issues to enhance overall network performance and flood resilience.

Junqiang Xia, Yifei Cheng, Meirong Zhou et al.

A general increase in the bankfull width and depth is found in downstream reaches because of upstream damming, especially in the braided reach of the Lower Yellow River (LYR), but the magnitude of bank erosion and its relation with bed incision remain little explored. Here based on long-term measured cross-sectional profiles (1999–2020), a quantitative method is proposed to estimate the bank erosion volume in the braided reach of the Lower Yellow River, with the contribution of bank erosion to the channel scour volume further determined. A quantitative relation was developed and calibrated between bank erosion width and bed incision depth, using the sediment continuity equation and measured data. The results indicate that: (i) significant bank erosion and bed incision processes are prevalent in the braided reach and its sub-reaches, with the bankfull widths increasing by 317–511 m and the bankfull depths increasing by 1.9–2.4 m in these reaches after the operation of the Xiaolangdi (XLD) Reservoir in 1999. Bank erosion has been dominant over bank accretion at more than 71% of the sections in the braided reach, with the most active bank deformation detected in the middle sub-reach. (ii) The cumulative bank erosion volumes temporally increased and spatially decreased, with the value of 1.80×108 m3 in the upper sub-reach (R1), 1.52×108 m3 in the middle sub-reach (R2), 1.08×108 m3 in the lower sub-reach (R3), and 4.40×108 m3 in the whole braided reach during the period of 1999–2020. Bank erosion contributed 33% of the cumulative channel scour volume in the braided reach, with a close relation developed between cumulative bank erosion volume and the previous 5-year average incoming sediment coefficient during flood seasons. (iii) A close inverse relation exists between bank erosion and bed incision in the whole braided reach and its sub-reaches, with the coefficients of determination greater than 0.90, which indicated that bank erosion hindered the process of bed incision. If there was no bank erosion after 1999, the cumulative bed incision depth would increase by at least 0.7 m in each reach. Furthermore, a similar quantitative relation was also applied to calculate the cumulative bed incision depth and bank erosion width in the braided reach during the period of 1960–1964 (the first stage after operation of the Sanmenxia Reservoir). Quite high accuracy was achieved in this analysis, with the coefficient of determination being equal to 0.96.

C. Montalvo, P. Tamagnone, E. Sañudo et al.

ABSTRACT 2D/1D dual drainage models are one of the most useful tools for studying urban pluvial flooding. However, the accuracy of these models depends on data quality and completeness. This study assesses the effects of sewer network data completeness on the results of the 2D/1D free distribution model Iber‐SWMM. The research is conducted in two case studies: Differdange (Luxembourg) and Osuna (Spain), considering six different return period storms. Different scenarios of data completeness were generated by simplifying the original sewer network, based on two characteristics of the conduit segments: the Strahler Order number and the length. Each scenario was evaluated by comparing the maximum flood extent maps obtained. The results indicate that the lower the degree of data completeness, the higher the overestimation of the maximum flood extent. For 80% completeness, the False Alarm Ratio is less than 0.05, but it can increase exponentially to over 0.30 when network completeness drops to 20%. However, if the available information includes the most important conduits, such as the main collectors, errors are minimal. Furthermore, if the data on surface elements (inlets) is also complete, the accuracy of flood modeling is maintained compared to the complete data scenario. These results can contribute to the simplification of flood model setup in large urban areas, where not always complete sewer network data sets are available and information preprocessing can be complex and time‐consuming, and the computation of the network in SWMM can become a bottleneck in the simulation.

Xiaolong Song, Haijue Xu, Yuchuan Bai

Rivers significantly influence delta morphology and sedimentation patterns. However, the dynamic effects of rivers on the upper neck areas of subaerial deltas, which are the complex zones connecting main channels to distributary networks, remain understudied. In this research, the impacts of discharge variation on cross-sectional topography within the upper neck area of a laboratory-scale subaerial delta were examined via an integrated shallow water jet (SWJ)–long short-term memory (LSTM) modeling approach that synergistically couples SWJ equations incorporating analytical velocity distributions and parameterized bedload transport relationships with LSTM networks and gradient boosting for data-driven enhancements. Laboratory experiments, which provide detailed topographic measurements, were used for model calibration and validation. We investigated stepwise, periodic, and stochastic discharge alteration scenarios. The results revealed a fundamental pattern of spatially differentiated morphodynamic sensitivity within the upper neck area. The section farthest upstream consistently exhibited relative stability. In stark contrast, the mid-sections (spanning approximately 20%–30% of the total delta length from the inlet) emerged as the primary loci of morphological change, consistently demonstrating robust switching behaviors between pronounced erosion and deposition regimes under varying discharge regimes. In contrast, the section farthest downstream showed a more integrated and dampened response. This distinct switching mechanism within defined mid-sections, rather than diffuse variability, constituted a key finding regarding the mechanism by which the upper neck area could fundamentally process discharge fluctuations. Specifically, discharge decreases typically led to localized scouring and enhanced channelization, particularly within these active mid-sections. Conversely, increases in discharge induced increasingly complex responses involving erosion and deposition, with the specific outcome being dependent on the precise location within these mid-sections and on the nature of discharge alteration. The core components of the morphological evolution of the delta were further evaluated by the finding that the magnitude, rate, and timing of discharge changes (e.g., rapid exponential changes and slow logistic decreases), along with the amplitude of periodic fluctuations, significantly governed the intensity and characteristics of this switching behavior and the resultant morphology. Increasingly pronounced effects were observed under rapid exponential changes, slow logistic decreases, and large periodic amplitudes. Under stochastic discharge, the mean reversion rate and long-term mean volatility of discharge exerted complex, spatially variable influences on the mean bed elevation change, highlighting their critical roles in shaping morphology, whereas the volatility had a more subtle and discharge-dependent impact. Thus, this research revealed not only variability but also a spatially organized response framework featuring critical zones and specific mechanisms, such as mid-section switching, governed by identifiable hydraulic parameters. The findings offered practical insights into delta management, climate adaptation, and environmental assessment, strengthening our understanding of fluvial–deltaic interactions and supporting ecosystem sustainability.

Phoebe King, Rosalind H. Bark, Andrew Lovett

ABSTRACT The demand for catchment‐based flood management to adapt to climate change is growing, with natural flood management (NFM) receiving increasing attention. NFM has implications for the ‘providers’ of land for measures upstream (the farmers) and the ‘beneficiaries’ of flood reduction downstream (the public). The misalignment of interests from these stakeholder groups may pose a challenge for flood risk managers during the delivery of NFM at the catchment scale. Considering this, a rapid evidence assessment (REA) of 60 peer‐reviewed articles was undertaken. This REA provides an overview of catchment perspectives, compares farmer and public preferences for NFM design, and explores key determinants of scheme acceptance. The public expressed positive perceptions and willingness to pay for NFM, with preferences for measures with large water storage capacity that deliver co‐benefits alongside flood management objectives. For farmers, NFM schemes that contributed to on‐farm conditions, for example, soil stability, were seen as positive, but overall, their willingness to adopt measures was limited. Nevertheless, knowledge of NFM among both groups strongly determined its acceptance. This suggests that resolving misaligned values will require policymakers and practitioners to work with these stakeholders on NFM design and farmer incentives to secure the delivery of future schemes.

Huhu Cui, Jungang Luo, Xue Yang et al.

ABSTRACT Mountainous river basins, typically located in river source areas, are characterized by steep terrain and dynamic landforms. These regions experience diverse climates due to topographic uplift, making them susceptible to frequent flash floods. The rapid onset and brief response time of flash floods pose significant challenges for achieving accurate and timely forecasting within limited warning periods. Deep learning models have emerged as powerful tools for high‐precision streamflow forecasting. This study develops an LSTM‐based multi‐sliding window flood forecasting model for various lead times and applies it to the Qinling Mountains watershed, with an emphasis on analyzing the model's interpretability. Results from the Maduwang Basin demonstrate the model's excellent performance in flood prediction for 1‐ and 3‐h lead times. While incorporating historical data can enhance model performance for long lead times, excessive historical inputs may be detrimental. Historical runoff significantly influences model performance. However, its contribution neither consistently increases with temporal proximity to the prediction time nor remains uniformly positive. The contribution of input features varies across different flood stages and can be explained by existing hydrological knowledge. This research demonstrates the potential of deep learning for flood forecasting in mountainous basins while providing insights into the interpretation of deep learning models. This provides scientific support for flood warning systems and emergency management.

Rory Cornelius Smith, Andrew Paul Barnes, Jingjing Wang et al.

Abstract This study introduces image‐based classification techniques to identify whether trash screens in urban rivers are blocked. The study obtained 755 images from a CCTV camera surveying a trash screen located on an urban river at Tongwynlais in Cardiff. Manual quality control reduced the dataset to 577 images, labelled as either blocked (80%) or unblocked (20%). The performance of a logistic regression for classification of images was investigated using three different subsets of the labelled images: (1) the original dataset, (2) a balanced but under‐sampled dataset with equal number of blocked and unblocked images, and (3) an augmented dataset with an equal number of blocked and unblocked images using Gaussian noise augmentation to increase the number of unblocked images. Results show that our data‐augmentation method enhanced model accuracy by 8%, successfully classifying images as blocked or unblocked with an accuracy of 88%; by overcoming the bias in the dataset these results also highlight potential solutions to overcome the challenges of operating this methodology across a network of cameras. This enables authorities in both data rich and data scarce regions the ability to take advantage of machine learning to open up the next generation of a distributed, data‐driven flood warning systems, protecting people, infrastructure and the environment.

Daniela Rodríguez Castro, Kasra Rafiezadeh Shahi, Nivedita Sairam et al.

ABSTRACT Flash floods cause high numbers of casualties and enormous economic damage. Good knowledge of the damage processes is crucial for the implementation of effective flash flood risk management. However, little is known about the damage processes that occur during flash floods, despite their severity. To gain more knowledge, independent data collection initiatives were carried out in the affected areas of Belgium and Germany after the 2021 floods. The resulting datasets include 420 damaged residential buildings in the Vesdre valley in Belgium, 277 in the Ahr valley in Rhineland‐Palatinate (Germany) and 332 in North Rhine‐Westphalia (Germany). A total of 30 potential damage‐influencing variables were harmonized across the regions, providing valuable insights into hazard characteristics, the vulnerability of exposed assets, the coping capacity of inhabitants, and socio‐economic factors. Machine learning‐based analysis reveals the significant importance of hazard variables, such as water depth and sediment transport, particularly for building damage. In addition to these, exposure (living area) and physical vulnerability factors (building type and wall type) also play a role in determining building damage across the affected regions. For content damage, besides water depth and living area, socio‐economic vulnerability (ownership status of the building) and emergency measures were found to be important predictors. These key drivers of building and content damage from flash floods can be utilized to develop more accurate damage models, thereby improving flash flood risk assessments, enhancing risk communication, and supporting better preparedness strategies.

Kosei Tsuji, Ichiro Maruta, Kenji Fujimoto et al.

Nonlinear Model Predictive Control (NMPC) offers a powerful approach for controlling complex nonlinear systems, yet faces two key challenges. First, accurately modeling nonlinear dynamics remains difficult. Second, variables directly related to control objectives often cannot be directly measured during operation. Although high-cost sensors can acquire these variables during model development, their use in practical deployment is typically infeasible. To overcome these limitations, we propose a Predictive Virtual Sensor Identification (PVSID) framework that leverages temporary high-cost sensors during the modeling phase to create virtual sensors for NMPC implementation. We validate PVSID on a Two-Degree-of-Freedom (2-DoF) direct-drive robotic arm with complex joint interactions, capturing tip position via motion capture during modeling and utilize an Inertial Measurement Unit (IMU) in NMPC. Experimental results show our NMPC with identified virtual sensors achieves precise tip trajectory tracking without requiring the motion capture system during operation. PVSID offers a practical solution for implementing optimal control in nonlinear systems where the measurement of key variables is constrained by cost or operational limitations.

Massimiliano de Sa, Pio Ong, Aaron D. Ames

Control barrier functions (CBFs) have a well-established theory in Euclidean spaces, yet still lack general formulations and constructive synthesis tools for systems evolving on manifolds common in robotics and aerospace applications. In this paper, we develop a general theory of geometric CBFs on bundles and, for control-affine systems, recover the standard optimization-based CBF controllers and their smooth analogues. Then, by generalizing kinetic energy-based CBF backstepping to Riemannian manifolds, we provide a constructive CBF synthesis technique for geometric mechanical systems, as well as easily verifiable conditions under which it succeeds. Further, this technique utilizes mechanical structure to avoid computations on higher-order tangent bundles. We demonstrate its application to an underactuated satellite on SO(3).

Jackson G. Ernesto, Eugenio B. Castelan, Walter Lucia

This paper presents a technique for designing output feedback controllers for constrained linear parameter-varying systems that are subject to persistent disturbances. Specifically, we develop an incremental parameter-varying output feedback control law to address control rate constraints, as well as state and control amplitude constraints. The proposal is based on the concept of robust positively invariant sets and applies the extended Farkas' lemma to derive a set of algebraic conditions that define both the control gains and a robust positively invariant polyhedron that satisfies the control and state constraints. These algebraic conditions are formulated into a bilinear optimization problem aimed at determining the output feedback gains and the associated polyedral robust positively invariant region. The obtained controller ensures that any closed-loop trajectory originating from the polyhedron converges to another smaller inner polyhedral set around the origin in finite time, where the trajectory remains ultimately bounded regardless of the persistent disturbances and variations in system parameters. Furthermore, by including the sizes of the two polyhedral sets inside the objective function, the proposed optimization can also jointly enlarge the outer set while minimizing the inner one. Numerical examples are presented to demonstrate the effectiveness of our proposal in managing the specified constraints, disturbances, and parameter variations.

Andrea Iannelli

The problem of designing adaptive stepsize sequences for the gradient descent method applied to convex and locally smooth functions is studied. We take an adaptive control perspective and design update rules for the stepsize that make use of both past (measured) and future (predicted) information. We show that Lyapunov analysis can guide in the systematic design of adaptive parameters striking a balance between convergence rates and robustness to computational errors or inexact gradient information. Theoretical and numerical results indicate that closed-loop adaptation guided by system theory is a promising approach for designing new classes of adaptive optimization algorithms with improved convergence properties.

Shuyu Yang, Dawen Yang, Jinsong Chen et al.

Abstract Large-scale reservoirs play an essential role in water resources management for agriculture irrigation, water supply and flood controls. However, we need robust reservoir operation systems under both normal flow and extreme flow conditions. In this study, we applied recurrent neural networks (RNN) to simulate the operation of three multi-purpose reservoirs located in the upper Chao Phraya River basin. Two reservoirs have the function of multiannual flow regulation and one has the function of incomplete annual regulation. The goal of this study is to explore the applicability of RNN models for operation of reservoirs with multiannual flow regulation under different flow regimes, especially under extreme floods and droughts. We used three RNNs, namely nonlinear autoregressive models with exogenous input (NARX), long short-term memory (LSTM) and genetic algorithm based NAXR (GA-NAXR) for reservoir operation based on historical data. For real-time water resources management, an accurate inflow forecast is required to provide a real-time reservoir outflow, and thus we also carried out a real-time reservoir operation using the RNN and the inflow forecast by a distributed hydrological model. Results show that (1) GA-NARX has the highest accuracy among three RNNs and is more stable than the original NARX by optimizing the initial conditions, although it takes longer training time than NARX and LSTM; (2) GA-NARX-based operation model is effective under extreme floods and droughts; and (3) the real-time operation system combining the GA-NARX and the distributed hydrological model has reasonable accuracy in both wet season and dry season. RNN-based operation model developed in this study has potential applicability in practical water management, and the model combining the hydrological prediction is specially useful for real-time reservoir operation.

Chrissy Mitchell

Andrew Lamperski, Tyler Lekang

Neural networks are regularly employed in adaptive control of nonlinear systems and related methods of reinforcement learning. A common architecture uses a neural network with a single hidden layer (i.e. a shallow network), in which the weights and biases are fixed in advance and only the output layer is trained. While classical results show that there exist neural networks of this type that can approximate arbitrary continuous functions over bounded regions, they are non-constructive, and the networks used in practice have no approximation guarantees. Thus, the approximation properties required for control with neural networks are assumed, rather than proved. In this paper, we aim to fill this gap by showing that for sufficiently smooth functions, ReLU networks with randomly generated weights and biases achieve $L_{\infty}$ error of $O(m^{-1/2})$ with high probability, where $m$ is the number of neurons. It suffices to generate the weights uniformly over a sphere and the biases uniformly over an interval. We show how the result can be used to get approximations of required accuracy in a model reference adaptive control application.

Eunhyek Joa, Francesco Borrelli

We propose a Model Predictive Control (MPC) with a single-step prediction horizon to approximate the solution of infinite horizon optimal control problems with the expected sum of convex stage costs for constrained linear uncertain systems. The proposed method aims to enhance a given sub-optimal controller, leveraging data to achieve a nearly optimal solution for the infinite horizon problem. The method is built on two techniques. First, we estimate the expected values of the convex costs using a computationally tractable approximation, achieved by sampling across the space of disturbances. Second, we implement a data-driven approach to approximate the optimal value function and its corresponding domain, through systematic exploration of the system's state space. These estimates are subsequently used to calculate the terminal cost and terminal set within the proposed MPC. We prove recursive feasibility, robust constraint satisfaction, and convergence in probability to the target set. Furthermore, we prove that the estimated value function converges to the optimal value function in a local region. The effectiveness of the proposed MPC is illustrated with detailed numerical simulations and comparisons with a value iteration method and a Learning MPC that minimizes a certainty equivalent cost.

Kenshiro Oguri

This paper presents a robust path-planning framework for safe spacecraft autonomy under uncertainty and develops a computationally tractable formulation based on convex programming. We utilize chance-constrained control to formulate the problem. It provides a mathematical framework to solve for a sequence of control policies that minimizes a probabilistic cost under probabilistic constraints with a user-defined confidence level (e.g., safety with 99.9% confidence). The framework enables the planner to directly control state distributions under operational uncertainties while ensuring the vehicle safety. This paper rigorously formulates the safe autonomy problem, gathers and extends techniques in literature to accommodate key cost/constraint functions that often arise in spacecraft path planning, and develops a tractable solution method. The presented framework is demonstrated via two representative numerical examples: safe autonomous rendezvous and orbit maintenance in cislunar space, both under uncertainties due to navigation error from Kalman filter, execution error via Gates model, and imperfect force models.

Yen-Yu Chiu, Yi-Hung Wu, Kuei-Lin Fu et al.

The Hutoubi Reservoir and its mainstream, Huyuan Stream, in the southern mountainous region of Taiwan, have experienced riverbed sedimentation and flood disasters for the past 150 years. In addition to climate change, it is necessary to scientifically consider its regulation for the next hundred years. This study adopted a collaborative approach, involving industry, government, and academia, using Nature-based Solutions (NbS) to enhance ecosystem services. The solution layout is constructed by widening the channel and constructing additional farm ponds and wetlands. An hydraulic simulation indicated that flood control was addressed. The restoration project would create diverse aquatic habitats by simulating and evaluating the distribution of ecological biotopes, using porous materials as revetments. It provided urban residents with forest leisure and recreational sites and supported the local agricultural and forestry products. The restoration has propagated local culture and created environmental and professional education. Therefore, ecological services are enhanced regarding regulation, support, provision, and culture. This pilot study, led by researchers, aimed to promote comprehensive management concepts considering all stakeholders and their active participation. We integrated NbS into the watershed and its river system as a pathway for facing the challenges of rapid urbanization and climate change and improving ecosystem services.