Approximate Bayesian computation (ABC) relaxes the need to derive explicit likelihood functions required by formal Bayesian analysis. However, the high computational cost of evaluating models limits the application of Bayesian inference in hydrological modeling. In this paper, multivariate adaptive regression splines (MARS) are used to expedite the ABC calibration process. The MARS model is trained using 6,561 runoff simulations generated by the SWAT model and subsequently replaces the SWAT model to calculate the objective functions in ABC and multi-objective evolutionary algorithm (MOEA). In experiments, MARS can successfully reproduce the runoff time series simulations of the SWAT model at a low time cost, with a runoff variance determination coefficient of 0.90 as compared to the Monte Carlo method. MARS-assisted ABC can quickly and accurately estimate the parameter distributions of the SWAT model. The comparison of ABC with non-Bayesian MOEAs helps in the selection of an appropriate calibration approach.
{"title":"Multivariate adaptive regression splines-assisted approximate Bayesian computation for calibration of complex hydrological models","authors":"Jinfeng Ma, Ruonan Li, Hua Zheng, Weifeng Li, Kai-Xia Rao, Yanzheng Yang, Bo Wu","doi":"10.2166/hydro.2024.232","DOIUrl":"https://doi.org/10.2166/hydro.2024.232","url":null,"abstract":"\u0000 Approximate Bayesian computation (ABC) relaxes the need to derive explicit likelihood functions required by formal Bayesian analysis. However, the high computational cost of evaluating models limits the application of Bayesian inference in hydrological modeling. In this paper, multivariate adaptive regression splines (MARS) are used to expedite the ABC calibration process. The MARS model is trained using 6,561 runoff simulations generated by the SWAT model and subsequently replaces the SWAT model to calculate the objective functions in ABC and multi-objective evolutionary algorithm (MOEA). In experiments, MARS can successfully reproduce the runoff time series simulations of the SWAT model at a low time cost, with a runoff variance determination coefficient of 0.90 as compared to the Monte Carlo method. MARS-assisted ABC can quickly and accurately estimate the parameter distributions of the SWAT model. The comparison of ABC with non-Bayesian MOEAs helps in the selection of an appropriate calibration approach.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139611271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most of the existing water quality models for water distribution networks assume complete mixing at junctions. Albeit few models offer the possibility to consider incomplete mixing (IM) at junctions, most of them were developed under laboratory conditions and for equal pipe size junctions. In real-world distribution networks, however, cross junctions of 150 × 100 × 150 × 100 mm or 100 × 150 × 150 × 150 mm are common, yet no model has been developed for these configurations. This paper presents a new equation to compute concentrations in cross junction outlets while considering IM for six cross junction configurations, including unequal pipe sizes and 150 mm pipes. For each cross junction configuration, mixing was studied under 25 flow scenarios in the laboratory and 40 simulated flow scenarios using OpenFOAM software. Two new flow rate ratios were selected as independent variables to compute different outlet concentrations. For two specific cross junctions with equal pipe sizes, the root-mean-squared error between the observed and simulated concentrations of the newly developed model was 0.02, while it was 0.05 and 0.07, respectively, for the AZRED IM model and the analytical IM model by Shao et al. (2014)).
{"title":"Improving incomplete mixing modeling for junctions of water distribution networks","authors":"Reza Yousefian, Sophie Duchesne","doi":"10.2166/hydro.2024.041","DOIUrl":"https://doi.org/10.2166/hydro.2024.041","url":null,"abstract":"\u0000 \u0000 Most of the existing water quality models for water distribution networks assume complete mixing at junctions. Albeit few models offer the possibility to consider incomplete mixing (IM) at junctions, most of them were developed under laboratory conditions and for equal pipe size junctions. In real-world distribution networks, however, cross junctions of 150 × 100 × 150 × 100 mm or 100 × 150 × 150 × 150 mm are common, yet no model has been developed for these configurations. This paper presents a new equation to compute concentrations in cross junction outlets while considering IM for six cross junction configurations, including unequal pipe sizes and 150 mm pipes. For each cross junction configuration, mixing was studied under 25 flow scenarios in the laboratory and 40 simulated flow scenarios using OpenFOAM software. Two new flow rate ratios were selected as independent variables to compute different outlet concentrations. For two specific cross junctions with equal pipe sizes, the root-mean-squared error between the observed and simulated concentrations of the newly developed model was 0.02, while it was 0.05 and 0.07, respectively, for the AZRED IM model and the analytical IM model by Shao et al. (2014)).","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of this research was to numerically reproduce bedload transport processes in channel bifurcations and thereby evaluate the methodology and feasibility of 3D-computational fluid dynamics (CFD) bedload transport simulations. This was carried out by numerically replicating two physical model investigations of channel bifurcations: research conducted by Bulle in 1926 and a large-scale channel bifurcation investigated at the Kassel University hydraulic laboratory. The numerical results were evaluated by comparing bedload distributions in the channel areas, formations of bedload depositions and development of bedload over time. The numerical simulations of Bulle's model were conducted for different boundary conditions, numerical parameters, sediment grain sizes, flow rates, flow distributions on the two channel branches and for longer running simulations. The investigations of the Kassel University bifurcation were carried out for different sediment types, which influences the numerical parameters, as well as for different flow distributions and channel width/depth ratios. Finally, structures for deflection of bedload into the straight channel were investigated. The results showed that bedload movement can be successfully simulated with 3D-CFD models, even in complex hydraulic conditions. Based on the obtained results, important indicators and recommendations for the application of 3D-CFD bedload transport simulations were acquired.
{"title":"3D-CFD analysis of bedload transport in channel bifurcations","authors":"Tino Kostić, Yuanjie Ren, Stephan Theobald","doi":"10.2166/hydro.2024.175","DOIUrl":"https://doi.org/10.2166/hydro.2024.175","url":null,"abstract":"\u0000 \u0000 The aim of this research was to numerically reproduce bedload transport processes in channel bifurcations and thereby evaluate the methodology and feasibility of 3D-computational fluid dynamics (CFD) bedload transport simulations. This was carried out by numerically replicating two physical model investigations of channel bifurcations: research conducted by Bulle in 1926 and a large-scale channel bifurcation investigated at the Kassel University hydraulic laboratory. The numerical results were evaluated by comparing bedload distributions in the channel areas, formations of bedload depositions and development of bedload over time. The numerical simulations of Bulle's model were conducted for different boundary conditions, numerical parameters, sediment grain sizes, flow rates, flow distributions on the two channel branches and for longer running simulations. The investigations of the Kassel University bifurcation were carried out for different sediment types, which influences the numerical parameters, as well as for different flow distributions and channel width/depth ratios. Finally, structures for deflection of bedload into the straight channel were investigated. The results showed that bedload movement can be successfully simulated with 3D-CFD models, even in complex hydraulic conditions. Based on the obtained results, important indicators and recommendations for the application of 3D-CFD bedload transport simulations were acquired.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139614368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Siddharth Seshan, Dirk Vries, Jasper N. Immink, Alex van der Helm, Johann Poinapen
The operation of smart wastewater treatment plants (WWTPs) is increasingly paramount in improving effluent quality, facilitating resource recovery and reducing carbon emissions. To achieve these objectives, sensors, monitoring systems, and artificial intelligence (AI)-based models are increasingly being developed and utilised for decision support and advanced control. One of the important aspects of the adoption of advanced data-driven control of WWTPs is real-time data validation and reconciliation (DVR), especially for sensor data. This research demonstrates and evaluates real-time AI-based data quality control methods, i.e. long short-term memory (LSTM) autoencoder (AE) models, to reconcile faulty sensor signals in WWTPs as compared to autoregressive integrated moving average (ARIMA) models. The DVR procedure is aimed at anomalies resulting from data acquisition issues and sensor faults. Anomaly detection precedes the reconciliation procedure using models that capture short-time dynamics (SD) and (relatively) long-time dynamics (LD). Real data from an operational WWTP are used to test the DVR procedure. To address the reconciliation of prolonged anomalies, the SD is aggregated with an LD model by exponential weighting. For reconciling single-point anomalies, both ARIMA and LSTM AEs showed high accuracy, while the accuracy of reconciliation regresses quickly with an increasing forecasting horizon for prolonged anomalous events.
{"title":"LSTM-based autoencoder models for real-time quality control of wastewater treatment sensor data","authors":"Siddharth Seshan, Dirk Vries, Jasper N. Immink, Alex van der Helm, Johann Poinapen","doi":"10.2166/hydro.2024.167","DOIUrl":"https://doi.org/10.2166/hydro.2024.167","url":null,"abstract":"\u0000 \u0000 The operation of smart wastewater treatment plants (WWTPs) is increasingly paramount in improving effluent quality, facilitating resource recovery and reducing carbon emissions. To achieve these objectives, sensors, monitoring systems, and artificial intelligence (AI)-based models are increasingly being developed and utilised for decision support and advanced control. One of the important aspects of the adoption of advanced data-driven control of WWTPs is real-time data validation and reconciliation (DVR), especially for sensor data. This research demonstrates and evaluates real-time AI-based data quality control methods, i.e. long short-term memory (LSTM) autoencoder (AE) models, to reconcile faulty sensor signals in WWTPs as compared to autoregressive integrated moving average (ARIMA) models. The DVR procedure is aimed at anomalies resulting from data acquisition issues and sensor faults. Anomaly detection precedes the reconciliation procedure using models that capture short-time dynamics (SD) and (relatively) long-time dynamics (LD). Real data from an operational WWTP are used to test the DVR procedure. To address the reconciliation of prolonged anomalies, the SD is aggregated with an LD model by exponential weighting. For reconciling single-point anomalies, both ARIMA and LSTM AEs showed high accuracy, while the accuracy of reconciliation regresses quickly with an increasing forecasting horizon for prolonged anomalous events.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139617399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stelios G. Vrachimis, Demetrios G. Eliades, Marios M. Polycarpou
A significant challenge when attempting to regulate the spatial-temporal concentration of a disinfectant in a water distribution network is the large and uncertain delay between the time that the chemical is injected at the input node and the time that the concentration is measured at the monitoring output nodes. Uncertain time delays are due to varying water flows, which depend mainly on consumer water demands. Existing approaches cannot guarantee that the concentration of the disinfectant will remain within a specified range at the output, even though bounds on time-delay uncertainty may be known. In this work, given bounded water-flow uncertainty, we use the input–output modeling approach to develop a disinfectant scheduling methodology that guarantees a bounded output disinfectant concentration. The proposed methodology creates an input–output model uncertainty characterization by utilizing estimated bounds on water-quality states using the backtracking approach. An optimization problem is formulated and solved to find an input schedule that keeps the disinfectant concentration within predefined bounds for a specified time horizon. Simulation results in two case studies where water demands varied between ±20% of their nominal value show that the proposed scheduler is able to avoid lower bound violations of disinfectant concentration.
{"title":"Disinfection scheduling in water distribution networks considering input time-delay uncertainty","authors":"Stelios G. Vrachimis, Demetrios G. Eliades, Marios M. Polycarpou","doi":"10.2166/hydro.2024.099","DOIUrl":"https://doi.org/10.2166/hydro.2024.099","url":null,"abstract":"\u0000 A significant challenge when attempting to regulate the spatial-temporal concentration of a disinfectant in a water distribution network is the large and uncertain delay between the time that the chemical is injected at the input node and the time that the concentration is measured at the monitoring output nodes. Uncertain time delays are due to varying water flows, which depend mainly on consumer water demands. Existing approaches cannot guarantee that the concentration of the disinfectant will remain within a specified range at the output, even though bounds on time-delay uncertainty may be known. In this work, given bounded water-flow uncertainty, we use the input–output modeling approach to develop a disinfectant scheduling methodology that guarantees a bounded output disinfectant concentration. The proposed methodology creates an input–output model uncertainty characterization by utilizing estimated bounds on water-quality states using the backtracking approach. An optimization problem is formulated and solved to find an input schedule that keeps the disinfectant concentration within predefined bounds for a specified time horizon. Simulation results in two case studies where water demands varied between ±20% of their nominal value show that the proposed scheduler is able to avoid lower bound violations of disinfectant concentration.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139617759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rhys Philips, Katelyn Ann Johnson, A. P. Barnes, Thomas Rodding Kjeldsen
This study has utilised National Oceanic and Atmospheric Administration (NOAA) NCEP/NCAR Reanalysis 1 project meteorological data and the HYSPLIT model to extract the air parcel trajectories for selected historical extreme rainfall events in South Africa. The k-means unsupervised machine learning algorithm has been used to cluster the resulting trajectories, and from this, the spatial origin of moisture for each of the rainfall events has been determined. It has been demonstrated that rainfall events on the east coast with moisture originating from the Indian Ocean have distinctly larger average maximum daily rainfall magnitudes (279 mm) compared to those that occur on the west coast with Atlantic Ocean influences (149 mm) and those events occurring in the central plateau (150 mm) where moisture has been continentally recirculated. Further, this study has suggested new metrics by which the HYSPLIT trajectories may be assessed and demonstrated the applicability of trajectory clustering in a region not previously studied. This insight may in future facilitate improved early warning systems based on monitoring of atmospheric systems, and an understanding of rainfall magnitudes and origins can be used to improve the prediction of design floods for infrastructure design.
{"title":"Identifying the pathways of extreme rainfall in South Africa using storm trajectory analysis and unsupervised machine learning techniques","authors":"Rhys Philips, Katelyn Ann Johnson, A. P. Barnes, Thomas Rodding Kjeldsen","doi":"10.2166/hydro.2023.261","DOIUrl":"https://doi.org/10.2166/hydro.2023.261","url":null,"abstract":"This study has utilised National Oceanic and Atmospheric Administration (NOAA) NCEP/NCAR Reanalysis 1 project meteorological data and the HYSPLIT model to extract the air parcel trajectories for selected historical extreme rainfall events in South Africa. The k-means unsupervised machine learning algorithm has been used to cluster the resulting trajectories, and from this, the spatial origin of moisture for each of the rainfall events has been determined. It has been demonstrated that rainfall events on the east coast with moisture originating from the Indian Ocean have distinctly larger average maximum daily rainfall magnitudes (279 mm) compared to those that occur on the west coast with Atlantic Ocean influences (149 mm) and those events occurring in the central plateau (150 mm) where moisture has been continentally recirculated. Further, this study has suggested new metrics by which the HYSPLIT trajectories may be assessed and demonstrated the applicability of trajectory clustering in a region not previously studied. This insight may in future facilitate improved early warning systems based on monitoring of atmospheric systems, and an understanding of rainfall magnitudes and origins can be used to improve the prediction of design floods for infrastructure design.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139166252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the benefits of using mathematical optimization for reservoir operation based on the assumed availability of short-term runoff forecasts. The novelty is the inclusion of the SSARR hydrological routing as optimization constraints in multiple time step optimization, where the routing coefficients are adjusted dynamically as functions of the channel flows. The paper shows significant reduction to downstream peak flows in flood-prone areas even with a forecast horizon of only 2 days, and it also includes the results of testing the effects of different lengths of forecasting horizons on model results. The case study is conducted on the Damodar River Basin in the Indian State of West Bengal, where basin development started in the 1950s, with flood protection of the downstream river valley as the highest management priority, in addition to water supply and hydro power. The solution methodology and the model results presented in this paper pave the way for eventual introduction to automated management of reservoir outflows that could revolutionize water resources industry in much the same way that auto-pilot and driverless cars are revolutionizing the transportation industry, assuming that runoff forecasting capabilities continue to improve.
{"title":"Advances in using mathematical optimization to manage floods with assessment of possible benefits using a case study","authors":"Nesa Ilich, Ashoke Basistha","doi":"10.2166/hydro.2023.247","DOIUrl":"https://doi.org/10.2166/hydro.2023.247","url":null,"abstract":"This paper presents the benefits of using mathematical optimization for reservoir operation based on the assumed availability of short-term runoff forecasts. The novelty is the inclusion of the SSARR hydrological routing as optimization constraints in multiple time step optimization, where the routing coefficients are adjusted dynamically as functions of the channel flows. The paper shows significant reduction to downstream peak flows in flood-prone areas even with a forecast horizon of only 2 days, and it also includes the results of testing the effects of different lengths of forecasting horizons on model results. The case study is conducted on the Damodar River Basin in the Indian State of West Bengal, where basin development started in the 1950s, with flood protection of the downstream river valley as the highest management priority, in addition to water supply and hydro power. The solution methodology and the model results presented in this paper pave the way for eventual introduction to automated management of reservoir outflows that could revolutionize water resources industry in much the same way that auto-pilot and driverless cars are revolutionizing the transportation industry, assuming that runoff forecasting capabilities continue to improve.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139215036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The hydraulic jump is a phenomenon that occurs in open channels. In past studies, hydraulic jumps over smooth and macrorough beds have been investigated to enhance energy dissipation, but triangular macroroughness, specifically the right-angled triangular macroroughness, has not been dealt with. The objective of this article is to numerically investigate submerged hydraulic jumps over right angle and isosceles triangular macrorough beds. To achieve this, a numerical model based on computational fluid dynamics (CFD) has been utilized. Numerically obtained jump characteristics such as submerged depth ratio, tailwater depth ratio, longitudinal velocity profile, flow pattern in the cavity region, and energy dissipation have been presented in detail. In particular, initial Froude number reduction in both tailwater and submerged depth ratios as well as an increase in the energy dissipation of submerged hydraulic jumps have been noticed on isosceles triangular macroroughness with different arrangements, as compared to smooth beds. The present numerical model has been validated with the experimental model, and the mean error between the two for submerged depth and tailwater depth ratios was found to be below 6%. This confirms the adequacy of the present CFD model in predicting relevant submerged hydraulic jump characteristics over macrorough beds.
{"title":"Numerical study of submerged hydraulic jumps over triangular macroroughnesses","authors":"Harshit Kumar Jayant, Bharat Jhamnani","doi":"10.2166/hydro.2023.302","DOIUrl":"https://doi.org/10.2166/hydro.2023.302","url":null,"abstract":"The hydraulic jump is a phenomenon that occurs in open channels. In past studies, hydraulic jumps over smooth and macrorough beds have been investigated to enhance energy dissipation, but triangular macroroughness, specifically the right-angled triangular macroroughness, has not been dealt with. The objective of this article is to numerically investigate submerged hydraulic jumps over right angle and isosceles triangular macrorough beds. To achieve this, a numerical model based on computational fluid dynamics (CFD) has been utilized. Numerically obtained jump characteristics such as submerged depth ratio, tailwater depth ratio, longitudinal velocity profile, flow pattern in the cavity region, and energy dissipation have been presented in detail. In particular, initial Froude number reduction in both tailwater and submerged depth ratios as well as an increase in the energy dissipation of submerged hydraulic jumps have been noticed on isosceles triangular macroroughness with different arrangements, as compared to smooth beds. The present numerical model has been validated with the experimental model, and the mean error between the two for submerged depth and tailwater depth ratios was found to be below 6%. This confirms the adequacy of the present CFD model in predicting relevant submerged hydraulic jump characteristics over macrorough beds.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139224464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Killian Gleeson, S. Husband, John Gaffney, J. Boxall
Deriving insight from the increasing volume of water quality time series data from drinking water distribution systems is complex and is usually situation- and individual-specific. This research used crowd-sourcing exercises involving groups of domain experts to identify features of interest within turbidity time series data from operational systems. The resulting labels provide insight and a novel benchmark against which algorithmic approaches to mimic the human interpretation could be evaluated. Reflection on the results of the labelling exercises resulted in the proposal of a turbidity event scale consisting of advisory <2 NTU, alert 2 < NTU < 4, and alarm >4 NTU levels to inform utility response. Automation was designed to enable event detection within these categories. A time-based averaging approach, calculating averages based on data at the same time of day, was found to be most effective for identifying low-level (<2 NTU) events. Simple flat-line event detection was sufficient to identify higher-level alert and alarm events. The automation of event detection and categorisation presented here provides the opportunity to gain actionable insight to safeguard drinking water quality from aging infrastructure.
{"title":"Algorithms to mimic human interpretation of turbidity events from drinking water distribution systems","authors":"Killian Gleeson, S. Husband, John Gaffney, J. Boxall","doi":"10.2166/hydro.2023.159","DOIUrl":"https://doi.org/10.2166/hydro.2023.159","url":null,"abstract":"Deriving insight from the increasing volume of water quality time series data from drinking water distribution systems is complex and is usually situation- and individual-specific. This research used crowd-sourcing exercises involving groups of domain experts to identify features of interest within turbidity time series data from operational systems. The resulting labels provide insight and a novel benchmark against which algorithmic approaches to mimic the human interpretation could be evaluated. Reflection on the results of the labelling exercises resulted in the proposal of a turbidity event scale consisting of advisory <2 NTU, alert 2 < NTU < 4, and alarm >4 NTU levels to inform utility response. Automation was designed to enable event detection within these categories. A time-based averaging approach, calculating averages based on data at the same time of day, was found to be most effective for identifying low-level (<2 NTU) events. Simple flat-line event detection was sufficient to identify higher-level alert and alarm events. The automation of event detection and categorisation presented here provides the opportunity to gain actionable insight to safeguard drinking water quality from aging infrastructure.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shanshan Li, Guiying Shen, Abbas Parsaie, Guodong Li, Dingye Cao
In this study, first, support vector machine (SVM) and three optimization algorithms are used to develop a discharge coefficient (Cd) prediction model for the semi-circular side weir (SCSW). After that, we derived the input and output parameters of the model by dimensionless analysis as the ratio of the flow depth at the weir crest point upstream to the side weir diameter (h1/D), the ratio of main channel width to side weir diameter (B/D), the ratio of side weir height to side weir diameter (P/D), upstream of side weir Froude number (Fr), and Cd. The sensitivity coefficients for dimensionless parameters to Cd were calculated based on Sobol's method. The research shows that SVM and genetic algorithm have high prediction accuracy and generalization ability; the average error and maximum error were 0.08 and 2.47%, respectively, which were about 95.72 and 60.86% lower compared with the traditional empirical model. The first-order sensitivity coefficients S1 and global sensitivity coefficients Si of h1/D, B/D, P/D, and Fr were 0.35, 0.07, 0.13, and 0.02; 0.63, 0.25, 0.30, and 0.32, respectively. h1/D has a significant effect on Cd. In particular, when h1/D < 0.24 and 0.48 < Fr < 0.58, 0.67 < Fr < 0.72, the discharge capacity of the SCSW is relatively large.
{"title":"Discharge modeling and characteristic analysis of semi-circular side weir based on the soft computing method","authors":"Shanshan Li, Guiying Shen, Abbas Parsaie, Guodong Li, Dingye Cao","doi":"10.2166/hydro.2023.268","DOIUrl":"https://doi.org/10.2166/hydro.2023.268","url":null,"abstract":"In this study, first, support vector machine (SVM) and three optimization algorithms are used to develop a discharge coefficient (Cd) prediction model for the semi-circular side weir (SCSW). After that, we derived the input and output parameters of the model by dimensionless analysis as the ratio of the flow depth at the weir crest point upstream to the side weir diameter (h1/D), the ratio of main channel width to side weir diameter (B/D), the ratio of side weir height to side weir diameter (P/D), upstream of side weir Froude number (Fr), and Cd. The sensitivity coefficients for dimensionless parameters to Cd were calculated based on Sobol's method. The research shows that SVM and genetic algorithm have high prediction accuracy and generalization ability; the average error and maximum error were 0.08 and 2.47%, respectively, which were about 95.72 and 60.86% lower compared with the traditional empirical model. The first-order sensitivity coefficients S1 and global sensitivity coefficients Si of h1/D, B/D, P/D, and Fr were 0.35, 0.07, 0.13, and 0.02; 0.63, 0.25, 0.30, and 0.32, respectively. h1/D has a significant effect on Cd. In particular, when h1/D < 0.24 and 0.48 < Fr < 0.58, 0.67 < Fr < 0.72, the discharge capacity of the SCSW is relatively large.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139244497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：