Efficient flood risk assessment and communication are essential for responding to increasingly recurrent flash floods. However, access to high-end data center computing is limited for stakeholders. This study evaluates the accuracy-speed trade-off of a hydraulic model by (i) assessing the potential acceleration of high-performance computing in PCs versus server-CPUs and GPUs, (ii) examining computing time evaluation and prediction indicators, and (iii) identifying variables controlling the computing time and their impact on the 2D hydrodynamic models' accuracy using an actual flash flood event as a benchmark. GPU-computing is found to be 130× and 55× faster than standard and parallelized CPU-computing, respectively, saving up to 99.5% of the computing time. The model's number of elements had the most significant impact, with <150,000 cells showing the best accuracy-speed trade-off. Using a PC equipped with a GPU enables almost real-time hydrodynamic information, democratizing flood data and facilitating interactive flood risk analysis.
{"title":"Investigating optimal 2D hydrodynamic modeling of a recent flash flood in a steep Norwegian river using high-performance computing","authors":"A. Moraru, Nils Rüther, O. Bruland","doi":"10.2166/hydro.2023.012","DOIUrl":"https://doi.org/10.2166/hydro.2023.012","url":null,"abstract":"\u0000 \u0000 Efficient flood risk assessment and communication are essential for responding to increasingly recurrent flash floods. However, access to high-end data center computing is limited for stakeholders. This study evaluates the accuracy-speed trade-off of a hydraulic model by (i) assessing the potential acceleration of high-performance computing in PCs versus server-CPUs and GPUs, (ii) examining computing time evaluation and prediction indicators, and (iii) identifying variables controlling the computing time and their impact on the 2D hydrodynamic models' accuracy using an actual flash flood event as a benchmark. GPU-computing is found to be 130× and 55× faster than standard and parallelized CPU-computing, respectively, saving up to 99.5% of the computing time. The model's number of elements had the most significant impact, with <150,000 cells showing the best accuracy-speed trade-off. Using a PC equipped with a GPU enables almost real-time hydrodynamic information, democratizing flood data and facilitating interactive flood risk analysis.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46127874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
River water level prediction (WLP) plays an important role in flood control, navigation, and water supply. In this study, a WaveNet-based convolutional neural network (WCNN) with a lightweight structure and good parallelism was developed to improve the prediction accuracy and time effectiveness of WLP. It was applied to predict 1/2/3 days the water levels at the Waizhou gauging station of the Ganjiang River (GR) in China, and it was compared with two recurrent neural networks (long short-term memory (LSTM) and gated recurrent unit (GRU)). The results showed that the WCNN model achieved the best prediction performance with the fewest training parameters and time. Compared with the LSTM and GRU models in the 1-day ahead prediction, the training parameters were reduced from 73,851 and 55,851 to 32,937, respectively. The root mean square error (RMSE) was reduced from 0.071 and 0.076 to 0.057, respectively. The mean absolute error (MAE) was reduced from 0.052 and 0.059 to 0.038, respectively. The Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) both increased to 0.998. This result indicated that the improved model was more efficient for WLP.
{"title":"A WaveNet-based convolutional neural network for river water level prediction","authors":"Jun Chen, Yan Huang, Teng Wu, Jing Yan","doi":"10.2166/hydro.2023.174","DOIUrl":"https://doi.org/10.2166/hydro.2023.174","url":null,"abstract":"\u0000 \u0000 River water level prediction (WLP) plays an important role in flood control, navigation, and water supply. In this study, a WaveNet-based convolutional neural network (WCNN) with a lightweight structure and good parallelism was developed to improve the prediction accuracy and time effectiveness of WLP. It was applied to predict 1/2/3 days the water levels at the Waizhou gauging station of the Ganjiang River (GR) in China, and it was compared with two recurrent neural networks (long short-term memory (LSTM) and gated recurrent unit (GRU)). The results showed that the WCNN model achieved the best prediction performance with the fewest training parameters and time. Compared with the LSTM and GRU models in the 1-day ahead prediction, the training parameters were reduced from 73,851 and 55,851 to 32,937, respectively. The root mean square error (RMSE) was reduced from 0.071 and 0.076 to 0.057, respectively. The mean absolute error (MAE) was reduced from 0.052 and 0.059 to 0.038, respectively. The Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) both increased to 0.998. This result indicated that the improved model was more efficient for WLP.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44375371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydraulic engineering applications require a good knowledge of turbulent behaviour in non-prismatic channels. This paper aims to predict turbulent behaviour using the large-eddy simulation (LES) method. The model channel has a warped transition. We perform two-phase LES of free-surface flow and validate the results using experimental data and benchmark solution. We discuss rigorous strategies for model set-up, parameter selection and parametric value assignment, including parameters in the spectrum synthesiser (SS) and vortex method (VM) for inlet turbulence. The predicted flow displays complex structures due to eddy motions translated from upstream and locally generated by asymmetrical separation in the transition. The history of the flow dynamics may affect the flow development. The predicted velocity, energy spectrum, root-mean-square error, hit-rate and factor-of-two compare well with measurements and benchmark solution. Mapping mean-velocity distribution from experimental data, combined with SS, gives satisfactory inlet condition; alternatively, a 1/7th power-law for the mean-velocity, combined with VM, is acceptable. This paper uses the Okubo–Weiss parameter to delineate 3D instantaneous coherent structures. The LES methods are reliable, efficient and cost-effective. As compared to the simulation of prismatic channels, the flow dynamics in non-prismatic channels exhibit flow separation and turbulence interactions, which increase the flow-complexity, while offering results with crucial practical applications.
{"title":"Large-eddy simulation of free-surface turbulent flow in a non-prismatic channel","authors":"Ruirui Zeng, S. S. Li","doi":"10.2166/hydro.2023.018","DOIUrl":"https://doi.org/10.2166/hydro.2023.018","url":null,"abstract":"\u0000 \u0000 Hydraulic engineering applications require a good knowledge of turbulent behaviour in non-prismatic channels. This paper aims to predict turbulent behaviour using the large-eddy simulation (LES) method. The model channel has a warped transition. We perform two-phase LES of free-surface flow and validate the results using experimental data and benchmark solution. We discuss rigorous strategies for model set-up, parameter selection and parametric value assignment, including parameters in the spectrum synthesiser (SS) and vortex method (VM) for inlet turbulence. The predicted flow displays complex structures due to eddy motions translated from upstream and locally generated by asymmetrical separation in the transition. The history of the flow dynamics may affect the flow development. The predicted velocity, energy spectrum, root-mean-square error, hit-rate and factor-of-two compare well with measurements and benchmark solution. Mapping mean-velocity distribution from experimental data, combined with SS, gives satisfactory inlet condition; alternatively, a 1/7th power-law for the mean-velocity, combined with VM, is acceptable. This paper uses the Okubo–Weiss parameter to delineate 3D instantaneous coherent structures. The LES methods are reliable, efficient and cost-effective. As compared to the simulation of prismatic channels, the flow dynamics in non-prismatic channels exhibit flow separation and turbulence interactions, which increase the flow-complexity, while offering results with crucial practical applications.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43510255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydraulic transient analysis allows the condition assessment of pipeline systems by the measurement of a system's transient pressure response subject to input pressure excitations. The detection of a pressure wave's arrival time and amplitude at one or more sections can be used to detect unexpected anomalies, such as leaks, blockages, or corroded sections. Wave separation approaches, based on signal processing techniques involving two sensors, enable a directional attribution to any measured pressure perturbations. Being able to determine the direction of origin of a perturbation through a signal-splitting approach greatly facilitates anomaly detection through the resolution of this ambiguity. The signal-splitting procedure can be sensitive to the analysis conditions (i.e. the signal processing procedure used, the presence of noise within the signal, and the spacing of the sensors) and, as a result, produce spurious results. This paper explores this issue and proposes, and analyses, a range of strategies to improve the signal-splitting results. The strategies explored involve the consideration of alternative time and frequency-domain formulations; the use of filters and wavelet to condition the signal; and processing the time-shifted differenced signal as opposed to the original raw signal. Results are presented for a range of numerical and laboratory systems.
{"title":"Separation of pressure signals caused by waves traveling in opposite directions","authors":"Marco Ferrante, Aaron Zecchin","doi":"10.2166/hydro.2023.021","DOIUrl":"https://doi.org/10.2166/hydro.2023.021","url":null,"abstract":"\u0000 Hydraulic transient analysis allows the condition assessment of pipeline systems by the measurement of a system's transient pressure response subject to input pressure excitations. The detection of a pressure wave's arrival time and amplitude at one or more sections can be used to detect unexpected anomalies, such as leaks, blockages, or corroded sections. Wave separation approaches, based on signal processing techniques involving two sensors, enable a directional attribution to any measured pressure perturbations. Being able to determine the direction of origin of a perturbation through a signal-splitting approach greatly facilitates anomaly detection through the resolution of this ambiguity. The signal-splitting procedure can be sensitive to the analysis conditions (i.e. the signal processing procedure used, the presence of noise within the signal, and the spacing of the sensors) and, as a result, produce spurious results. This paper explores this issue and proposes, and analyses, a range of strategies to improve the signal-splitting results. The strategies explored involve the consideration of alternative time and frequency-domain formulations; the use of filters and wavelet to condition the signal; and processing the time-shifted differenced signal as opposed to the original raw signal. Results are presented for a range of numerical and laboratory systems.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42716531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu Shao, Kun Li, Tuqiao Zhang, Y. Jeffrey Yang, Shipeng Chu
Abstract The normal probability density function (PDF) is widely used in parameter estimation in the modeling of dynamic systems, assuming that the random variables are distributed at infinite intervals. However, in practice, these random variables are usually distributed in a finite region confined by the physical process and engineering practice. In this study, we address this issue through the application of truncated normal PDF. This method avoids a non-differentiable problem inherited in the truncated normal PDF at the truncation points, a limitation that can limit the use of analytical methods (e.g., Gaussian approximation). A data assimilation method with the derived formula is proposed to describe the probability of parameter and measurement noise in the truncated space. In application to a water distribution system (WDS), the proposed method leads to estimating nodal water demand and hydraulic pressure key to hydraulic and water quality model simulations. Application results to a hypothetical and a large field WDS clearly show the superiority of the proposed method in parameter estimation for WDS simulations. This improvement is essential for developing real-time hydraulic and water quality simulation and process control in field applications when the parameter and measurement noise are distributed in the finite region.
{"title":"Modeling of truncated normal distribution for estimating hydraulic parameters in water distribution systems: taking nodal water demand as an example","authors":"Yu Shao, Kun Li, Tuqiao Zhang, Y. Jeffrey Yang, Shipeng Chu","doi":"10.2166/hydro.2023.250","DOIUrl":"https://doi.org/10.2166/hydro.2023.250","url":null,"abstract":"Abstract The normal probability density function (PDF) is widely used in parameter estimation in the modeling of dynamic systems, assuming that the random variables are distributed at infinite intervals. However, in practice, these random variables are usually distributed in a finite region confined by the physical process and engineering practice. In this study, we address this issue through the application of truncated normal PDF. This method avoids a non-differentiable problem inherited in the truncated normal PDF at the truncation points, a limitation that can limit the use of analytical methods (e.g., Gaussian approximation). A data assimilation method with the derived formula is proposed to describe the probability of parameter and measurement noise in the truncated space. In application to a water distribution system (WDS), the proposed method leads to estimating nodal water demand and hydraulic pressure key to hydraulic and water quality model simulations. Application results to a hypothetical and a large field WDS clearly show the superiority of the proposed method in parameter estimation for WDS simulations. This improvement is essential for developing real-time hydraulic and water quality simulation and process control in field applications when the parameter and measurement noise are distributed in the finite region.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135299871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ying Zhao, Mayank Chadha, Nicholas Olsen, Elissa Yeates, Josh Turner, Guga Gugaratshan, Gu Qian, Michael D. Todd, Zhen Hu
Streamflow prediction of rivers is crucial for making decisions in watershed and inland waterways management. The US Army Corps of Engineers (USACE) uses a river routing model called RAPID to predict water discharges for thousands of rivers in the network for watershed and inland waterways management. However, the calibration of hydrological streamflow parameters in RAPID is time-consuming and requires streamflow measurement data which may not be available for some ungauged locations. In this study, we aim to address the calibration aspect of the RAPID model by exploring machine learning (ML)-based methods to facilitate efficient calibration of hydrological model parameters without the need for streamflow measurements. Various ML models are constructed and compared to learn a relationship between hydrological model parameters and various river parameters, such as length, slope, catchment size, percentage of vegetation, and elevation contours. The studied ML models include Gaussian process regression, Gaussian mixture copula, Random Forest, and XGBoost. This study has shown that ML models that are carefully constructed by considering causal and sensitive input features offer a potential approach that not only obtains calibrated hydrological model parameters with reasonable accuracy but also bypasses the current calibration challenges.
{"title":"Machine learning-enabled calibration of river routing model parameters","authors":"Ying Zhao, Mayank Chadha, Nicholas Olsen, Elissa Yeates, Josh Turner, Guga Gugaratshan, Gu Qian, Michael D. Todd, Zhen Hu","doi":"10.2166/hydro.2023.030","DOIUrl":"https://doi.org/10.2166/hydro.2023.030","url":null,"abstract":"\u0000 Streamflow prediction of rivers is crucial for making decisions in watershed and inland waterways management. The US Army Corps of Engineers (USACE) uses a river routing model called RAPID to predict water discharges for thousands of rivers in the network for watershed and inland waterways management. However, the calibration of hydrological streamflow parameters in RAPID is time-consuming and requires streamflow measurement data which may not be available for some ungauged locations. In this study, we aim to address the calibration aspect of the RAPID model by exploring machine learning (ML)-based methods to facilitate efficient calibration of hydrological model parameters without the need for streamflow measurements. Various ML models are constructed and compared to learn a relationship between hydrological model parameters and various river parameters, such as length, slope, catchment size, percentage of vegetation, and elevation contours. The studied ML models include Gaussian process regression, Gaussian mixture copula, Random Forest, and XGBoost. This study has shown that ML models that are carefully constructed by considering causal and sensitive input features offer a potential approach that not only obtains calibrated hydrological model parameters with reasonable accuracy but also bypasses the current calibration challenges.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"43 20","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41247321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ryan C. Johnson, S. J. Burian, C. Oroza, James Halgren, Trevor Irons, Danyal Aziz, Daniyal Hassan, Jiada Li, Carly Hansen, T. Kirkham, Jesse Stewart, Laura Briefer
Sustainable western US municipal water system (MWS) management depends on quantifying the impacts of supply and demand dynamics on system infrastructure reliability and vulnerability. Systems modeling can replicate the interactions but extensive parameterization, high complexity, and long development cycles present barriers to widespread adoption. To address these challenges, we develop the Machine Learning Water Systems Model (ML-WSM) – a novel application of data-driven modeling for MWS management. We apply the ML-WSM framework to the Salt Lake City, Utah water system, where we benchmark prediction performance on the seasonal response of reservoir levels, groundwater withdrawal, and imported water requests to climate anomalies at a daily resolution against an existing systems model. The ML-WSM accurately predicts the seasonal dynamics of all components; especially during supply-limiting conditions (KGE > 0.88, PBias < ±3%). Extreme wet conditions challenged model skill but the ML-WSM communicated the appropriate seasonal trends and relationships to component thresholds (e.g., reservoir dead pool). The model correctly classified nearly all instances of vulnerability (83%) and peak severity (100%), encouraging its use as a guidance tool that complements systems models for evaluating the influences of climate on MWS performance.
{"title":"Data-driven modeling of municipal water system responses to hydroclimate extremes","authors":"Ryan C. Johnson, S. J. Burian, C. Oroza, James Halgren, Trevor Irons, Danyal Aziz, Daniyal Hassan, Jiada Li, Carly Hansen, T. Kirkham, Jesse Stewart, Laura Briefer","doi":"10.2166/hydro.2023.170","DOIUrl":"https://doi.org/10.2166/hydro.2023.170","url":null,"abstract":"\u0000 \u0000 Sustainable western US municipal water system (MWS) management depends on quantifying the impacts of supply and demand dynamics on system infrastructure reliability and vulnerability. Systems modeling can replicate the interactions but extensive parameterization, high complexity, and long development cycles present barriers to widespread adoption. To address these challenges, we develop the Machine Learning Water Systems Model (ML-WSM) – a novel application of data-driven modeling for MWS management. We apply the ML-WSM framework to the Salt Lake City, Utah water system, where we benchmark prediction performance on the seasonal response of reservoir levels, groundwater withdrawal, and imported water requests to climate anomalies at a daily resolution against an existing systems model. The ML-WSM accurately predicts the seasonal dynamics of all components; especially during supply-limiting conditions (KGE > 0.88, PBias < ±3%). Extreme wet conditions challenged model skill but the ML-WSM communicated the appropriate seasonal trends and relationships to component thresholds (e.g., reservoir dead pool). The model correctly classified nearly all instances of vulnerability (83%) and peak severity (100%), encouraging its use as a guidance tool that complements systems models for evaluating the influences of climate on MWS performance.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44952209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sewage systems are essential for the efficient functioning of cities. Wastewater contains solids and organic matter that pose health risks, making it necessary to optimize the sewerage system design. In recent years, optimization tools have been introduced to minimize costs while still complying with regulations. Despite this, traditional designs still dominate, but the use of optimization methodologies can significantly reduce construction costs. For this reason, this research will compare the construction costs of a sewerage system designed optimally and one designed using traditional methods, to determine the cost difference between the two. In a sector of Bogota, Colombia, a sewerage system was already built and designed according to Colombian laws using traditional methodologies. The information from this area was used to implement the UTOPIA program, created by the Universidad de los Andes. This program uses the Shortest Path Problem with the Bellman-Ford algorithm to design the network and minimize costs. The results show that the optimized system was about 15% cheaper than the traditional one, and it ensured that all pipelines met the design restrictions. Optimized sewage systems are a useful alternative for ensuring universal access to safe drinking water, increasing sewerage coverage, and reducing problems associated with inadequate design.
{"title":"Economic comparison between an optimized and a traditional sewer system designs","authors":"M. A. González, J. Saldarriaga","doi":"10.2166/hydro.2023.027","DOIUrl":"https://doi.org/10.2166/hydro.2023.027","url":null,"abstract":"\u0000 \u0000 Sewage systems are essential for the efficient functioning of cities. Wastewater contains solids and organic matter that pose health risks, making it necessary to optimize the sewerage system design. In recent years, optimization tools have been introduced to minimize costs while still complying with regulations. Despite this, traditional designs still dominate, but the use of optimization methodologies can significantly reduce construction costs. For this reason, this research will compare the construction costs of a sewerage system designed optimally and one designed using traditional methods, to determine the cost difference between the two. In a sector of Bogota, Colombia, a sewerage system was already built and designed according to Colombian laws using traditional methodologies. The information from this area was used to implement the UTOPIA program, created by the Universidad de los Andes. This program uses the Shortest Path Problem with the Bellman-Ford algorithm to design the network and minimize costs. The results show that the optimized system was about 15% cheaper than the traditional one, and it ensured that all pipelines met the design restrictions. Optimized sewage systems are a useful alternative for ensuring universal access to safe drinking water, increasing sewerage coverage, and reducing problems associated with inadequate design.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41494985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To ensure the preservation of public health during periods of water distribution network (WDN) contamination, implementing effective consequence management (CM) plans is crucial. This study aimed to minimize the number of operational interventions and mitigate adverse effects on public health by considering WDN leakage and demand changes during contamination events. Surveys conducted during the contamination period revealed an impressive 88% reduction in water consumption. Subsequently, a real case study focusing on a segment of Tehran's WDN in Iran's capital city was conducted, examining four scenarios to test the proposed method. Without employing leakage and demand reduction strategies, the total contamination exposure amounted to approximately 184 kg. However, by incorporating water demand reduction, leakage, and their simultaneous simulation, maximum contamination exposures of 154.4, 171, and 124.4 kg were respectively achieved. Furthermore, it was found that the optimal CM plan required significantly different valve configurations. Neglecting demand changes and leaks in the CM plan led to inaccurate calculations regarding hydraulic and quality status, pollution levels in the network, and contamination exposure for WDN users; therefore, erroneous decision-making.
{"title":"Optimal consequence management of pollution intrusion into water distribution network considering demand variation and pipelines' leakage: a case study","authors":"Seyed Ghasem Razavi, S. Nazif, M. Ghorbani","doi":"10.2166/hydro.2023.003","DOIUrl":"https://doi.org/10.2166/hydro.2023.003","url":null,"abstract":"\u0000 \u0000 To ensure the preservation of public health during periods of water distribution network (WDN) contamination, implementing effective consequence management (CM) plans is crucial. This study aimed to minimize the number of operational interventions and mitigate adverse effects on public health by considering WDN leakage and demand changes during contamination events. Surveys conducted during the contamination period revealed an impressive 88% reduction in water consumption. Subsequently, a real case study focusing on a segment of Tehran's WDN in Iran's capital city was conducted, examining four scenarios to test the proposed method. Without employing leakage and demand reduction strategies, the total contamination exposure amounted to approximately 184 kg. However, by incorporating water demand reduction, leakage, and their simultaneous simulation, maximum contamination exposures of 154.4, 171, and 124.4 kg were respectively achieved. Furthermore, it was found that the optimal CM plan required significantly different valve configurations. Neglecting demand changes and leaks in the CM plan led to inaccurate calculations regarding hydraulic and quality status, pollution levels in the network, and contamination exposure for WDN users; therefore, erroneous decision-making.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48756240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate models of water withdrawal are crucial in anticipating the potential water use impacts of drought and climate change. Machine learning methods can simulate the complex, nonlinear relationship between water use and potential explanatory factors, but rarely incorporate the hierarchical nature of water use data. This work presents a novel approach for the prediction of water withdrawals across multiple usage sectors using an ensemble of models fit at different hierarchical levels. Models were fit at the facility and sectoral grouping levels, as well as across facility clusters defined by temporal water use characteristics. Using repeated holdout cross-validation and a dataset of over 300,000 observations of monthly water withdrawal across 1,509 facilities, it demonstrates that ensemble predictions led to statistically significant improvements in predictive performance in five of the eight sectors analyzed. The use of ensemble modeling resulted in lower predictive errors compared to facility models in 65% of facilities analyzed. The relative improvement gained by ensemble modeling was greatest for facilities with fewer observations and higher variance, indicating its potential value in predicting withdrawal for facilities with relatively short data records or data quality issues.
{"title":"Prediction of multi-sectoral longitudinal water withdrawals using hierarchical machine learning models","authors":"J. Shortridge","doi":"10.2166/hydro.2023.110","DOIUrl":"https://doi.org/10.2166/hydro.2023.110","url":null,"abstract":"\u0000 Accurate models of water withdrawal are crucial in anticipating the potential water use impacts of drought and climate change. Machine learning methods can simulate the complex, nonlinear relationship between water use and potential explanatory factors, but rarely incorporate the hierarchical nature of water use data. This work presents a novel approach for the prediction of water withdrawals across multiple usage sectors using an ensemble of models fit at different hierarchical levels. Models were fit at the facility and sectoral grouping levels, as well as across facility clusters defined by temporal water use characteristics. Using repeated holdout cross-validation and a dataset of over 300,000 observations of monthly water withdrawal across 1,509 facilities, it demonstrates that ensemble predictions led to statistically significant improvements in predictive performance in five of the eight sectors analyzed. The use of ensemble modeling resulted in lower predictive errors compared to facility models in 65% of facilities analyzed. The relative improvement gained by ensemble modeling was greatest for facilities with fewer observations and higher variance, indicating its potential value in predicting withdrawal for facilities with relatively short data records or data quality issues.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43538207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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