Showing papers in "Journal of Hydroinformatics in 2020"

Deep learning convolutional neural network in rainfall–runoff modelling

Abstract: This is the final draft, after peer-review, of a manuscript published in Journal of Hydroinformatics. The published version is available online at https://doi.org/10.2166/hydro.2020.095

Updating the neural network sediment load models using different sensitivity analysis methods: a regional application

Abstract: The amount of transported sediment load by streams is a vital but high nonlinear dynamic process in water resources management. In the current paper, two optimum predictive models subjected to artificial neural network (ANN) were developed. The employed inputs were then prioritized using diverse sensitivity analysis (SA) methods to address new updated but more efficient ANN structures. The models were found through the 263 processed datasets of three rivers in Idaho, USA using nine different measured flow and sediment variables (e.g., channel geometry, geomorphology, hydraulic) for a period of 11 years. The used parameters were selected based on the prior knowledge of the conventional analyses in which the effect of suspended load on bed load was also investigated. Analyzed accuracy performances using different criteria exhibited improved predictability in updated models which can lead to an advanced understanding of used parameters. Despite different SA methods being employed in evaluating model parameters, almost similar results were observed and then verified using relevant sensitivity indices. It was demonstrated that the ranked parameters using SA due to covering more uncertainties can be more reliable. Evaluated models using sensitivity indices showed that contribution of suspended load on predicted bed load is not significant. doi: 10.2166/hydro.2020.098 om http://iwaponline.com/jh/article-pdf/22/3/562/693089/jh0220562.pdf er 2021 Reza Asheghi Seyed Abbas Hosseini (corresponding author) Mojtaba Saneie Abbas Abbaszadeh Shahri Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran E-mail: abbas_hoseyni@srbiau.ac.ir

Riprap incipient motion for overtopping flows with machine learning models

Abstract: Riprap stones are frequently applied to protect rivers and channels against erosion processes. Many empirical equations have been proposed in the past to estimate the unit discharge at the failure circumstance of riprap layers. However, these equations lack general impact due to the limited range of experimental variables. To overcome these shortcomings, support vector machine (SVM), multivariate adaptive regression splines (MARS), and random forest (RF) techniques have been applied in this study to estimate the approach densimetric Froude number at the incipient motion of riprap stones. Riprap stone size, streambank slope, uniformity coefficient of riprap layer stone, specific density of stones, and thickness of riprap layer have been considered as controlling variables. Quantitative performances of the artificial intelligence (AI) models have been assessed by many statistical measures including: coefficient of correlation (R), root mean square error (RMSE), mean absolute error (MAE), and scatter index (SI). Statistical performance of AI models indicated that SVM model with radial basis function (RBF) kernel had better performance (SI1⁄4 0.37) than MARS (SI1⁄4 0.75) and RF (SI1⁄4 0.63) techniques. The proposed AI models performed better than existing empirical equations. From a parametric study the results demonstrated that the erosion-critical stone-referred Froude number (Fs,c) is mainly controlled by the streambank slope. doi: 10.2166/hydro.2020.129 ://iwaponline.com/jh/article-pdf/22/4/749/715149/jh0220749.pdf Mohammad Najafzadeh (corresponding author) Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced TechnologyKerman, P.O. Box 76315-116, Kerman, Iran E-mail: moha.najafzadeh@gmail.com Giuseppe Oliveto School of Engineering, University of Basilicata, Potenza, Italy

Optimal water quality sensor positioning in urban drainage systems for illicit intrusion identification

Abstract: In the last decade, the growth of the micro-industry in urban areas has produced an increase in the frequency of xenobiotic polluting discharges in drainage systems. Wastewater treatment plants are usually characterized by low removal efficiencies in respect of such pollutants, which may have an acute or cumulative impact on environmental and public health. To facilitate the early isolation of illicit intrusions, this study aims to develop an approach for positioning water quality sensors based on the Bayesian decision network (BDN). The analysis is focused on soluble conservative pollutants, such as metals. The proposed methodology incorporates several sources of information, including network topology, flows and non-formal ‘grey’ information about the possible locations of contamination sources. The methodology is tested using two sewer systems with increasing complexity: a literature scheme from the Storm Water Management Model (SWMM) manual and a real combined sewer in Italy. In both cases, the approach identifies the optimal sensor location gaining advantage from additional information, which reduces the computational effort needed to obtain the solution. In the real case, the application of the method yielded a better solution with regards to the real position of the implemented sensor network.

Prediction of sediment transport rates in gravel-bed rivers using Gaussian process regression

Abstract: Estimating sediment transport rate in rivers has high importance due to the difficulties and costs associated with its measurement, which has drawn the attention of experts in water engineering. In this study, Gaussian process regression (GPR) is applied to predict the sediment transport rate for 19 gravel-bed rivers in the United States. To compare the performance of GPR, the support vector machine (SVM) as a common type of kernel-based models was developed. Model inputs of sediment transport were prepared based on two scenarios: the first scenario considers only hydraulic characteristics and the second scenario was formed using hydraulic and sediment properties. Obtained results revealed that the GPR models present better performance compared to the SVM models and other empirical sediment transport formulas. Also, it was found that incorporating the second scenario as input led to better predictions. In addition, performing sensitivity analysis showed that the ratio of average velocity to shear flow velocity is the most effective parameter in predicting the sediment transport rate of gravel-bed rivers.

A multi-model integration method for monthly streamflow prediction: modified stacking ensemble strategy

Abstract: In this study, we evaluate elastic net regression (ENR), support vector regression (SVR), random forest (RF) and eXtreme Gradient Boosting (XGB) models and propose a modified multi-model integration method named a modified stacking ensemble strategy (MSES) for monthly streamflow forecasting. We apply the above methods to the Three Gorges Reservoir in the Yangtze River Basin, and the results show the following: (1) RF and XGB present better and more stable forecast performance than ENR and SVR. It can be concluded that the machine learning-based models have the potential for monthly streamflow forecasting. (2) The MSES can effectively reconstruct the original training data in the first layer and optimize the XGB model in the second layer, improving the forecast performance. We believe that the MSES is a computing framework worthy of development, with simple mathematical structure and low computational cost. (3) The forecast performance mainly depends on the size and distribution characteristics of the monthly streamflow sequence, which is still difficult to predict using only climate indices.

Hydrology@Home: a distributed volunteer computing framework for hydrological research and applications

Abstract: Web-based distributed volunteer computing enables scientists to constitute platforms that can be used for computational tasks by using potentially millions of computers connected to the internet. It is a widely used approach for many scientific projects, including the analysis of radio signals for signs of extraterrestrial intelligence and determining the mechanisms of protein folding. User adoption and clients' dependence on the desktop software present challenges in volunteer computing projects. This study presents a web-based volunteer computing framework for hydrological applications that requires only a web browser to participate in distributed computing projects. The framework provides distribution and scaling capabilities for projects with user bases of thousands of volunteers. As a case study, we tested and evaluated the proposed framework with a large-scale hydrological flood forecasting model.

High-performance computing in water resources hydrodynamics

Abstract: This work presents a vision of future water resources hydrodynamics codes that can fully utilize the strengths of modern high-performance computing (HPC). The advances to computing power, formerly driven by the improvement of central processing unit processors, now focus on parallel computing and, in particular, the use of graphics processing units (GPUs). However, this shift to a parallel framework requires refactoring the code to make efficient use of the data as well as changing even the nature of the algorithm that solves the system of equations. These concepts along with other features such as the precision for the computations, dry regions management, and input/output data are analyzed in this paper. A 2D multi-GPU flood code applied to a large-scale test case is used to corroborate our statements and ascertain the new challenges for the next-generation parallel water resources codes.

New stochastic modeling strategy on the prediction enhancement of pier scour depth in cohesive bed materials

Abstract: Scouring around the piers, especially in cohesive bed materials, is a fully stochastic phenomenon and a reliable prediction of scour depth is still a challenging concern for bridge designers. This study introduces a new stochastic model based on the integration of Group Method of Data Handling (GMDH) and Generalized Likelihood Uncertainty Estimation (GLUE) to predict scour depth around piers in cohesive soils. The GLUE approach is developed to estimate the related parameters whereas the GMDHmodel is used for the prediction target. To assess the adequacy of the GMDH-GLUE model, the conventional GMDH and genetic programming (GP) models are also developed for evaluation. Several statistical performance indicators are computed over both the training and testing phases for the prediction accuracy validation. Based on the attained numerical indicators, the proposed GMDHGLUE model revealed better predictability performance of pier scour depth against the benchmark models as well as several gathered literature studies. To provide an informative comparison among the proposed techniques (i.e. GMDH-GLUE, GMDH, and GP models), an improvement index (IM) is employed. Results indicated that the GMDH-GLUE model achieved IMtrain 1⁄4 6% and IMtest 1⁄4 3%, demonstrating satisfying performance improvement in comparison with the previously proposed

Water supply network pollution source identification by random forest algorithm

Abstract: A novel approach for identifying the source of contamination in a water supply network based on the random forest classifying algorithm is presented in this paper. The proposed method is tested on two different water distribution benchmark networks with different sensor placements. For each considered network, a considerable amount of contamination scenarios with randomly selected contamination parameters were simulated and water quality time series of network sensors were obtained. Pollution scenarios were defined by randomly generated pollution source location, pollution starting time, duration of injection and the chemical intensity of the pollutant. Sensor layout’s influence, demand uncertainty and imperfect sensor measurements were also investigated to verify the robustness of the method. The proposed approach shows high accuracy in localizing the potential sources of pollution, thus greatly reducing the complexity of the water supply network contamination detection problem.

Assessment of artificial intelligence models for calculating optimum properties of lined channels

Abstract: Lined channels with trapezoidal, rectangular and triangular sections are the most common manmade canals in practice. Since the construction cost plays a key role in water conveyance projects, it has been considered as the prominent factor in optimum channel designs. In this study, artificial neural networks (ANN) and genetic programming (GP) are used to determine optimum channel geometries for trapezoidal-family cross sections. For this purpose, the problem statement is treated as an optimization problem whose objective function and constraint are earthwork and lining costs and Manning’s equation, respectively. The comparison remarkably demonstrates that the applied artificial intelligence (AI) models achieved much closer results to the numerical benchmark solutions than the available explicit equations for optimum design of lined channels with trapezoidal, rectangular and triangular sections. Also, investigating the average of absolute relative errors obtained for determination of dimensionless geometries of trapezoidal-family channels using AI models shows that this criterion will not be more than 0.0013 for the worst case, which indicates the high accuracy of AI models in optimum design of trapezoidal channels.

Delft Dashboard: a quick set-up tool for hydrodynamic models

Abstract: The open-source program Delft Dashboard (DDB) is a graphical user interface designed to quickly create, edit input parameters and visualize model inputs for a number of hydrodynamic models, using private or publicly available local and global datasets. It includes a number of toolboxes that facilitate the generation of spatially varying inputs. These include new model schematizations (grids, bathymetry, boundary conditions, etc.), cyclonic wind fields and initial tsunami waves. The use of DDB can have significant benefits. It can save modellers considerable time and effort. Furthermore, the automated nature of both data collection and pre-processing within the program reduces the likelihood of errors that could occur when setting up models manually. Three case studies are presented: simulation of tides in the North Sea, storm surge and wave modelling under tropical cyclone conditions and the simulation of a tsunami. The test cases show that models created with DDB can be set up efficiently while maintaining a predictive skill that is only slightly lower than that of extensively calibrated models. doi: 10.2166/hydro.2020.092 om http://iwaponline.com/jh/article-pdf/22/3/510/693325/jh0220510.pdf er 2021 Maarten van Ormondt (corresponding author) Deltares, Marine and Coastal Systems, Boussinesqweg 1, 2629 HV Delft, The Netherlands E-mail: maarten.vanormondt@deltares.nl Kees Nederhoff Deltares-USA, 8601 Georgia Ave #508, Silver Spring, MD 20910, USA Ap van Dongeren Deltares, Marine and Coastal Systems, Boussinesqweg 1, 2629 HV Delft, The Netherlands

Assessment of seasonal and annual rainfall trend in Calabria (southern Italy) with the ITA method

Abstract: In this paper, an investigation on the temporal variability of seasonal and annual rainfall in the Calabria region (southern Italy) was carried out using a homogeneous and gap-filled monthly rainfall dataset of 129 rain gauges in the period 1951–2006. In particular, possible trends have been assessed by means of the Innovative Trend Analysis (ITA) technique, which allows the identification of a trend in the low, medium and high values of a series. Moreover, the results obtained with the ITA have been compared with the ones obtained with the Mann–Kendall test. These analyses have been performed in five rainfall zones (RZs) of the study area, characterized by different climatic conditions. As a result, both the methods evidenced a negative trend of the annual rainfall in the entire study area. On a seasonal scale, this negative tendency has been confirmed in autumn and winter although with some differences among the several RZs.

Numerical simulation of scour and flow field over movable bed induced by a submerged wall jet

Abstract: Dam construction continues its rapid expansion around the world primarily for the purpose of hydropower generation. One important consequence of such projects is local scour at the downstream of the dam caused by outflow of excess reservoir water through spillways or bottom outlets that is associated with high velocities. The scour development endangers the dam foundation and river banks and undermines the stability of the hydraulic structures. In this study, a detailed three-dimensional (3D) flow simulation is conducted to investigate the complex fluid–sediment interactions leading to the formation of the scour hole and ridge systems downstream of a nearbottom jet. Three different bed-load equations, including Meyer-Peter–Müller, Nielsen, and Van Rijn formulas, are applied for calculating the bed-load transport rate. Comparison with a series of available experimental data shows that the Meyer-Peter–Müller equation results in better predictions than the two other relations. The performance of different turbulence models to reproduce vertical profiles of velocity and scour characteristic against the experimental data were evaluated. The vertical and horizontal profiles of the scour hole-ridge system are also compared with the corresponding experimental ones. The numerical model satisfactorily reproduces the geometric parameters representing the scour hole. However, the model overestimates the length of the scour

Optimising wastewater treatment solutions for the removal of contaminants of emerging concern (CECs): a case study for application in India

Abstract: The aim of this study was to produce optimal wastewater treatment solutions to calculate the removal of different contaminants of emerging concern (CECs) found in developing countries. A new methodology was developed for a decision support tool (WaStewater Decision support OptiMiser, WiSDOM), which focuses on producing treatment solutions suited to treating water for reuse to Indian Water Quality Standards. WiSDOM-CEC analyses the removal of CECs through different treatment solutions and was also used to evaluate the performance of each treatment train solution in terms of the removal of conventional pollutants using multi-objective optimisation and multi-criteria decision analysis. Information was collected on different CECs across different regions of India, and the removal of 18 different CECs through 42 wastewater treatment unit processes for five different regions of India was analysed. Comparisons between similar categories of CECs, such as non-steroidal anti-inflammatory, showed that emerging contaminants all react differently with individual treatment options. For example, the removal of ibuprofen (IBP) and naproxen (NPX) varied from >80% and 0%, respectively, for a solution in Karnataka involving sedimentation, submerged aerated filter, ultrafiltration and nanofiltration. In Tamil Nadu, results ranged from 36.8% to 72% for diclofenac, 10.7% to 66.5% for IBP, and 0% for NPX.

Wavelet-based local mesh refinement for rainfall–runoff simulations

Abstract: A wavelet-based local mesh refinement (wLMR) strategy is designed to generate multiresolution and unstructured triangular meshes from real digital elevation model (DEM) data for efficient hydrological simulations at the catchment scale. The wLMR strategy is studied considering slope- and curvature-based refinement criteria to analyze DEM inputs: the slope-based criterion uses bed elevation data as input to the wLMR strategy, whereas the curvature-based criterion feeds the bed slope data into it. The performance of the wLMR meshes generated by these two criteria is compared for hydrological simulations; first, using three analytical tests with the systematic variation in topography types and then by reproducing laboratory- and real-scale case studies. The bed elevation on the wLMR meshes and their simulation results are compared relative to those achieved on the finest uniform mesh. Analytical tests show that the slope- and curvature-based criteria are equally effective with the wLMR strategy, and that it is easier to decide which criterion to take in relation to the (regular) shape of the topography. For the realistic case studies: (i) slope analysis provides a better metric to assess the correlation of a wLMR mesh to the fine uniform mesh and (ii) both criteria predict outlet hydrographs with a close predictive accuracy to that on the uniform mesh, but the curvature-based criterion is found to slightly better capture the channeling patterns of real DEM data.

Application of nature-inspired optimization algorithms to ANFIS model to predict wave-induced scour depth around pipelines

Abstract: Wave-induced scour depth below pipelines is a physically complex phenomenon, whose reliable prediction may be challenging for pipeline designers. This study shows the application of adaptive neuro-fuzzy inference system (ANFIS) incorporated with particle swarm optimization (ANFIS-PSO), ant colony (ANFIS-ACO), differential evolution (ANFIS-DE) and genetic algorithm (ANFIS-GA) and assesses the scour depth prediction performance and associated uncertainty in different scour conditions including live-bed and clear-water. To this end, the non-dimensional parameters Shields number (θ), Keulegan–Carpenter number (KC) and embedded depth to diameter of pipe ratio (e=D) are considered as prediction variables. Results indicate that the ANFIS-PSO model (Rlive bed 1⁄4 0:832 and Rclearwater 1⁄4 0:984) is the most accurate predictive model in both scour conditions when all three mentioned non-dimensional input parameters are included. Besides, the ANFIS-PSO model shows a better prediction performance than recently developed models. Based on the uncertainty analysis results, the prediction of scour depth is characterized by larger uncertainty in the clear-water condition, associated with both model structure and input variable combination, than in live-bed condition. Furthermore, the uncertainty in scour depth prediction for both live-bed and clear-water conditions is due more to the input variable combination (R-factorave 1⁄4 4:3) than it is due to the model structure (R-factorave 1⁄4 2:2).

Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions

Abstract: In this paper, one of the newest meta-heuristic algorithms, named artificial bee colony (ABC) algorithm, is used to solve the single-reservoir operation optimization problem. The simple and hydropower reservoir operation optimization problems of Dez reservoir, in southern Iran, have been solved here over 60, 240, and 480 monthly operation time periods considering two different decision variables. In addition, to improve the performance of this algorithm, two improved artificial bee colony algorithms have been proposed and these problems have been solved using them. Furthermore, in order to improve the performance of proposed algorithms to solve large-scale problems, two constrained versions of these algorithms have been proposed, in which in these algorithms the problem constraints have been explicitly satisfied. Comparison of the results shows that using the proposed algorithm leads to better results with low computational costs in comparison with other available methods such as genetic algorithm (GA), standard and improved particle swarm optimization (IPSO) algorithm, honey-bees mating optimization (HBMO) algorithm, ant colony optimization algorithm (ACOA), and gravitational search algorithm (GSA). Therefore, the proposed algorithms are capable algorithms to solve large reservoir operation optimization problems.

Finite volume model for the simulation of 1D unsteady river flow and water quality based on the WASP

Abstract: In this work, a one-dimensional (1D) finite volume numerical model for the unsteady simulation of the flow hydrodynamics and water quality is developed. The water dynamics is formulated with the 1D shallow water equations, and the water quality evolution is described by the Water Quality Analysis Simulation Program (WASP) model, allowing us to interpret and predict the transport and fate of various biochemical substances along any river reach. This combined system is solved with an explicit finite volume scheme based on Roe's linearization for the advection component of both the flow and the solute transport equations. The proposed model is able to consider temporal variations in tributaries and abstractions occurring in the river basin. This feature is transcendent in order to predict the chemical composition of natural water bodies during winter and summer periods, leading to an improvement in the agreement between computed and observed water quality evolutions. The combined model has been evaluated using literature tests in a steady state and a real-field case of the Ebro river (Spain), characterized by a marked unsteady regime. In the real case, we found that the water temperature was very sensitive to both the solar radiation and the average air temperature, requiring a careful calibration of these parameters. The numerical results are also demonstrated to be reasonably accurate, conservative and robust in real-scale field cases, showing that the model is able to predict the evolution of quality parameters as well as hydrodynamic variables in complex scenarios.

Deep learning under H2O framework: A novel approach for quantitative analysis of discharge coefficient in sluice gates

Abstract: Gates in dams and irrigation canals have been used for the purpose of controlling discharge or water surface regulation. To compute the discharge under a gate, discharge coefficient (Cd) should be first determined precisely. From a novel point of view, this study investigates the effect of sill shape under the vertical sluice gate on Cd using four artificial intelligence methods, which are used to estimate Cd, (i) random forest (RF), (ii) deep learning (DL), (iii) gradient boosting machine (GBM), and (iv) generalized linear model (GLM). A sluice gate along with twelve different forms of sills was fabricated and tested in the University of Tabriz, Iran. Different flow rates were considered in the hydraulic laboratory with four gate openings. As a result, a total of 180 runs could be tested. The results showed that the installation of sill under the vertical gate has a positive effect on flow discharge. Sill shapes can be characterized by their hydraulic radius (Rs). Sensitivity analysis among the dimensionless parameters proved that Rs/G (the ratio of the hydraulic radius of the sills with respect to the gate opening) has a significant role in the determination of Cd. A semi-circular sill shape has a more positive effect on the increase of Cd than the other shapes.

Attribution of growing season vegetation activity to climate change and human activities in the Three-River Headwaters Region, China

Abstract: Over the past century, vegetation change has been reported at global, national, and regional scales, accompanied by significant climate change and intensified human activities. Among the regions is the rangeland of the Three-River Headwaters Region (TRHR) in China. However, which factor dominates in causing vegetation change in this region is still under considerable debate, and how would the grasslands adapt to the changing environment is largely unknown. To address these issues, we attribute growing season vegetation activity to climate change and human activities, investigate the interactions among different driving variables, and explore the dynamic relationship between vegetation activity and the driving variables. We perform Mann–Kendall trend analysis, Pearson correlation analysis, and partial correlation analysis. The results indicate that the dominant factor for vegetation growth, during the period 1995–2014, was temperature for the southeastern and southern parts of the TRHR, precipitation for the western part, and solar radiation for the northeastern part. The regulation effects of temperature on precipitation and cloud cover contributed to vegetation growth, while grazing activity and population activity offset the positive contribution of climate change. The dynamic relationship between vegetation activity and the driving variables reflected the acclimatization and adaption processes of vegetation, which needs further investigation.

Optimal sensor placement for pipe burst detection in water distribution systems using cost–benefit analysis

Abstract: Fast detection of pipe burst in water distribution systems (WDSs) could improve customer satisfaction, increase the profits of water supply and more importantly reduce the loss of water resources. Therefore, sensor placement for pipe burst detection in WDSs has been a crucial issue for researchers and practitioners. This paper presents an economic evaluation indicator named as net cost based on cost–benefit analysis to solve the optimal pressure sensor placement problem. The net cost is defined as the sum of the normalized optimal detection uncovering rate and investment cost of sensors. The optimal detection uncovering rate and the optimal set of sensor locations are determined through a single-objective optimization model that maximizes the detection coverage rate under a fixed number of sensors. The optimal number of sensors is then determined by analyzing the relationship between the net cost and the number of sensors. The proposed method is demonstrated to be effective in determining both the optimal number of sensors and their locations on a benchmark network Net3. Moreover, the sensor accuracy and pipe burst flow magnitude are shown to be key uncertainties in determining the optimal number of sensors. doi: 10.2166/hydro.2020.158 om http://iwaponline.com/jh/article-pdf/22/3/606/692594/jh0220606.pdf er 2021 Mengke Zhao Chi Zhang Haixing Liu (corresponding author) Yuntao Wang School of Hydraulic Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China E-mail: hliu@dlut.edu.cn Guangtao Fu Center for Water Systems, College of Engineering, Mathematics, and Physical Sciences, University of Exeter, North Park Rd., Exeter EX4 4QF, UK

A simple method for water balance estimation based on the empirical method and remotely sensed evapotranspiration estimates

Abstract: Developing a methodology for water balance estimation is a significant challenge, especially in areas with little or no gauging. This is because direct measurements of all the water balance components are not feasible. To overcome this issue, we propose a simple methodology based on the predefined empirical relationship between remotely sensed evapotranspiration (ET), i.e. Moderate Resolution Imaging Spectroradiometer (MODIS) ET and groundwater recharge (GR), and readily available precipitation data at the monthly time step. The developed methodology was applied in seven catchments in NE Greece using time series of precipitation and remotely sensed ET from 2009 to 2019. The potential of the proposed method to accurately estimate the water balance was assessed by the comparison of the individual water balance components against modeled values. Three performance metrics were examined and indicated that the methodology produces a satisfactory outcome. Our results indicated mean ET accounting for approximately 54% of precipitation, mean GR of 24% and mean surface runoff approximately 22% of precipitation in the study area. The proposed approach was implemented using freely available remotely sensed products and the free R software for statistical computing and graphics, offering thus a convenient and inexpensive alternative for water balance estimation, even for basins with limited data availability. doi: 10.2166/hydro.2020.182 om http://iwaponline.com/jh/article-pdf/22/2/440/666248/jh0220440.pdf er 2021 George Falalakis School of Science and Technology, Hellenic Open University, Patras, Greece Alexandra Gemitzi (corresponding author) Department of Environmental Engineering, Faculty of Engineering, Democritus University of Thrace, V. Sofias 12, 67100 Xanthi, Greece E-mail: agkemitz@env.duth.gr

Optimisation of a fuzzy logic-based local real-time control system for mitigation of sewer flooding using genetic algorithms

Abstract: Urban flooding damages properties, causes economic losses and can seriously threaten public health. An innovative, fuzzy logic (FL)-based, local autonomous real-time control (RTC) approach for mitigating this hazard utilising the existing spare capacity in urban drainage networks has been developed. The default parameters for the control algorithm, which uses water level-based data, were derived based on domain expert knowledge and optimised by linking the control algorithm programmatically to a hydrodynamic sewer network model. This paper describes a novel genetic algorithm (GA) optimisation of the FL membership functions (MFs) for the developed control algorithm. In order to provide the GA with strong training and test scenarios, the compiled rainfall time series based on recorded rainfall and incorporating multiple events were used in the optimisation. Both decimal and integer GA optimisations were carried out. The integer optimisation was shown to perform better on unseen events than the decimal version with considerably reduced computational run time. The optimised FL MFs result in an average 25% decrease in the flood volume compared to those selected by experts for unseen rainfall events. This distributed, autonomous control using GA optimisation offers significant benefits over traditional RTC approaches for flood risk management.

Estimating extremely large amounts of missing precipitation data

Abstract: Accurate estimation of missing daily precipitation data remains a difficult task. A wide variety of methods exists for infilling missing values, but the percentage of gaps is one of the main factors limiting their applicability. The present study compares three techniques for filling in large amounts of missing daily precipitation data: spatio-temporal kriging (STK), multiple imputation by chained equations through predictive mean matching (PMM), and the random forest (RF) machine learning algorithm. To our knowledge, this is the first time that extreme missingness (>90%) has been considered. Different percentages of missing data and missing patterns are tested in a large dataset drawn from 112 rain gauges in the period 1975–2017. The results show that both STK and RF can handle extreme missingness, while PMM requires larger observed sample sizes. STK is the most robust method, suitable for chronological missing patterns. RF is efficient under random missing patterns. Model evaluation is usually based on performance and error measures. However, this study outlines the risk of just relying on these measures without checking for consistency. The RF algorithm overestimated daily precipitation outside the validation period in some cases due to the overdetection of rainy days under time-dependent missing patterns. doi: 10.2166/hydro.2020.127 om http://iwaponline.com/jh/article-pdf/22/3/578/692717/jh0220578.pdf er 2021 Héctor Aguilera (corresponding author) Carolina Guardiola-Albert Carmen Serrano-Hidalgo Research on Geological Resources, Geological Survey of Spain, Ríos Rosas 23, 28003, Madrid, Spain E-mail: h.aguilera@igme.es Carmen Serrano-Hidalgo School of Mining Engineering of Madrid, Technical University of Madrid, Ríos Rosas 21, 28003, Madrid, Spain

Optimization of dam's spillway design under climate change conditions

Abstract: The present research introduces a model to find the best shape of a dam’s spillway under climate change impacts, considering a benchmark problem (i.e., Ute Dam’s labyrinth spillway in the Canadian River watershed, New Mexico, USA). A spillway design is based not only on historical data but also on the future hydrologic events. Climate variables were predicted for the years 2021–2050 based on three representative concentration pathway (RCP2.6, RCP4.5, and RCP8.5) scenarios of the general circulation model from the fifth phase of the coupled model intercomparison project (CMIP5) using the statistical downscaling model. Streamflow at the USGS 07226500 streamgage was simulated by a rainfall–runoff model with predicted data. Instantaneous peak flow was estimated using an empirical method. Flood frequency analysis was used for the estimation of the design flood. The shuffled frogleaping algorithm (SFLA) is used to optimize a labyrinth spillway design and its results were compared with two other nature-inspired algorithms: invasive weed optimization (IWO) and cuckoo search (CS). The spillway was optimized once with the actual design flood (16,143 m/s) and again with the design flood under climate change (12,250 m/s). Results revealed that optimization with realistic design flood reduced the concrete volume of the spillway by 37% and under climate change by 43% using the SFLA.

Daily runoff forecasting based on data-augmented neural network model

Abstract: Accurate daily runoff prediction plays an important role in the management and utilization of water resources. In order to improve the accuracy of prediction, this paper proposes a deep neural network (CAGANet) composed of a convolutional layer, an attention mechanism, a gated recurrent unit (GRU) neural network, and an autoregressive (AR) model. Given that the daily runoff sequence is abrupt and unstable, it is difficult for a single model and combined model to obtain high-precision daily runoff predictions directly. Therefore, this paper uses a linear interpolation method to enhance the stability of hydrological data and apply the augmented data to the CAGANet model, the support vector machine (SVM) model, the long short-term memory (LSTM) neural network and the attentionmechanism-based LSTM model (AM-LSTM). The comparison results show that among the four models based on data augmentation, the CAGANet model proposed in this paper has the best prediction accuracy. Its Nash–Sutcliffe efficiency can reach 0.993. Therefore, the CAGANet model based on data augmentation is a feasible daily runoff forecasting scheme.

Agent-based modelling of pedestrian responses during flood emergency: mobility behavioural rules and implications for flood risk analysis

Abstract: An agent-based model (ABM) for simulating the interactions between flooding and pedestrians is augmented to more realistic model responses of evacuees during floodwater flow. In this version of the ABM, the crowd of pedestrians have different body heights and weight, and extra behavioural rules are added to incorporate pedestrians’ states of stability and walking speed in floodwater. The augmented ABM is applied to replicate an evacuation scenario for a synthetic test case of a flooded shopping centre. Simulation runs are performed with increasingly sophisticated configuration modes for the pedestrians’ behavioural rules. Simulation results are analysed based on spatial and temporal indicators informing on the dynamic variations of the flood risk states of the flooded pedestrians, i.e. in terms of a commonly used flood Hazard Rating (HR) metric, variable walking speed, and instability due to toppling and/or sliding. Our analysis reveals significantly prolonged evacuation times and risk exposure levels as the stability and walking speed behavioural rules become more sophisticated. Also, it allows us to identify more conservative HR thresholds of pedestrian instability in floodwater, and a new formula relating walking speed states to the HR for stable pedestrians in floodwater. Accompanying details for software accessibility are provided.

Characteristics of free and submerged hydraulic jumps over different macroroughnesses

Abstract: The present study deals with numerical simulations of the free and submerged hydraulic jumps over different shapes of roughness in various roughness arrangements and different Froude number conditions. The models were studied using three roughness shapes, i.e. triangular, square and semioval for 0.2< T/I< 0.5, where T and I are height and distance of roughness, respectively. The results showed that the numerical model is fairly well able to simulate the free and submerged jump characteristics. The effect of roughness plays a role in the reduction of the relative maximum velocity which is greater in the submerged jump. The thickness of the boundary layer for both free and submerged jumps decreases with increasing the distance between the roughnesses. Triangular macroroughness has a significant effect on the length of the jump and shortest length with respect to the other shapes. The reduction in the submerged depth ratio and tailwater depth ratio depends mainly on the space of the roughnesses. The highest shear stress and energy loss in both jumps occur in a triangular macroroughness (TR) with T/I1⁄4 0.50 compared to other ratios and modes. The numerical results were compared with previous studies and relationships with good correlation coefficients were presented for the mentioned parameters.

Decentralized ellipsoidal state estimation for linear model predictive control of an irrigation canal

Abstract: A centralized linear MPC is used to stabilize an irrigation system whose operation is represented by an integrator-delay model. Since not all the state variables can be measured, a decentralized ellipsoidal estimation strategy is proposed. This approach keeps the quality of a centralized estimation and reduces significantly the computation time for the systems considered. An adaptation of Test Canal 1, developed by the ASCE Task Committee on Canal Automation Algorithms, is used as a case study to show the performance of the proposed methodology. doi: 10.2166/hydro.2020.150 ://iwaponline.com/jh/article-pdf/22/3/593/692893/jh0220593.pdf L. P. Rodriguez National Scientific and Technical Research Council, (CONICET), San Juan, Argentina J. M. Maestre (corresponding author) E. F. Camacho Departament of Ingeniera de Sistemas y Automática, Universidad de Sevilla, Sevilla, Spain E-mail: pepemaestre@us.es M. C. Sánchez Planta Piloto de Ingeniera Química (UNS – CONICET), Bahía Blanca, Argentina