Abstract The discharge estimation in rivers is crucial in implementing flood management techniques and essential flood defence and drainage systems. During the normal flood season, water flows solely in the main channel. During a flood, rivers comprise a main channel and floodplains, collectively called a compound channel. Computing the discharge is challenging in non-prismatic compound channels where the floodplains converge or diverge in a longitudinal direction. Various soft computing techniques have nowadays become popular in the field of water resource engineering to solve these complex problems. This paper uses a hybrid soft computing technique – artificial neural network and particle swarm optimization (ANN–PSO) and multivariate adaptive regression splines (MARS) to model the discharge in non-prismatic compound open channels. The analysis considers nine non-dimensional parameters – bed slope, relative flow depth, relative longitudinal distance, hydraulic radius ratio, angle of convergence or divergence, flow aspect ratio, relative friction factor, and area ratio – as influencing factors. A gamma test is carried out to determine the optimal combination of input variables. The developed MARS model has produced satisfactory results, with a mean absolute percentage error (MAPE) of less than 7% and an R2 value of more than 0.90.
{"title":"Discharge estimation in a compound channel with converging and diverging floodplains using ANN–PSO and MARS","authors":"Divyanshu Shekhar, Bhabani Shankar Das, Kamalini Devi, Jnana Ranjan Khuntia, Tapas Karmaker","doi":"10.2166/hydro.2023.145","DOIUrl":"https://doi.org/10.2166/hydro.2023.145","url":null,"abstract":"Abstract The discharge estimation in rivers is crucial in implementing flood management techniques and essential flood defence and drainage systems. During the normal flood season, water flows solely in the main channel. During a flood, rivers comprise a main channel and floodplains, collectively called a compound channel. Computing the discharge is challenging in non-prismatic compound channels where the floodplains converge or diverge in a longitudinal direction. Various soft computing techniques have nowadays become popular in the field of water resource engineering to solve these complex problems. This paper uses a hybrid soft computing technique – artificial neural network and particle swarm optimization (ANN–PSO) and multivariate adaptive regression splines (MARS) to model the discharge in non-prismatic compound open channels. The analysis considers nine non-dimensional parameters – bed slope, relative flow depth, relative longitudinal distance, hydraulic radius ratio, angle of convergence or divergence, flow aspect ratio, relative friction factor, and area ratio – as influencing factors. A gamma test is carried out to determine the optimal combination of input variables. The developed MARS model has produced satisfactory results, with a mean absolute percentage error (MAPE) of less than 7% and an R2 value of more than 0.90.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135149379","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}
Akshita Bassi, Ajaz Ahmad Mir, Bimlesh Kumar, Mahesh Patel
Abstract A fundamental issue in the hydraulics of movable bed channels is the measurement of friction factor (λ), which represents the head loss because of hydraulic resistance. The execution of experiments in the laboratory hinders the predictability of λ over a short period of time. The major challenges that arise with traditional forecasting approaches are due to their subjective nature and reliance on various assumptions. Therefore, advanced machine learning (ML) and artificial intelligence approaches can be utilized to overcome this tedious task. Here, eight different ML techniques have been employed to predict the λ using eight different input features. To compare the performance of models, various error metrics have been assessed and compared. The graphical inferences from heatmap data visualization, Taylor diagram, sensitivity analysis, and parametric analysis with different input scenarios (ISs) have been carried out. Based on the outcome of the study, it has been observed that K Star in the IS1 with correlation coefficient (R2) value equal to 0.9716 followed by M5 Prime (0.9712) and Random Forest (0.9603) in IS2 and IS4, respectively, have provided better results as compared to the other ML models to predict λ in terms of least errors.
{"title":"A comprehensive study of various regressions and deep learning approaches for the prediction of friction factor in mobile bed channels","authors":"Akshita Bassi, Ajaz Ahmad Mir, Bimlesh Kumar, Mahesh Patel","doi":"10.2166/hydro.2023.246","DOIUrl":"https://doi.org/10.2166/hydro.2023.246","url":null,"abstract":"Abstract A fundamental issue in the hydraulics of movable bed channels is the measurement of friction factor (λ), which represents the head loss because of hydraulic resistance. The execution of experiments in the laboratory hinders the predictability of λ over a short period of time. The major challenges that arise with traditional forecasting approaches are due to their subjective nature and reliance on various assumptions. Therefore, advanced machine learning (ML) and artificial intelligence approaches can be utilized to overcome this tedious task. Here, eight different ML techniques have been employed to predict the λ using eight different input features. To compare the performance of models, various error metrics have been assessed and compared. The graphical inferences from heatmap data visualization, Taylor diagram, sensitivity analysis, and parametric analysis with different input scenarios (ISs) have been carried out. Based on the outcome of the study, it has been observed that K Star in the IS1 with correlation coefficient (R2) value equal to 0.9716 followed by M5 Prime (0.9712) and Random Forest (0.9603) in IS2 and IS4, respectively, have provided better results as compared to the other ML models to predict λ in terms of least errors.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591656","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}
Daniel Bezerra Barros, Rui Gabriel Souza, Gustavo Meirelles, Bruno Brentan
Abstract Leakages in water distribution networks (WDNs) affect the hydraulic state of the entire or a large part of the network. Statistical correlation computed among pressure sensors monitoring network nodes aids the detection and localization of such leaks. This opens the possibility to work with water network databases, where graph signal processing (GSP) tools aid in understanding changes in pressure signals due to leakages in the hydraulic system. This paper presents a methodology for time-varying pressure signals on graph structures. The core of this methodology is based on changing of pressure, due to leaks, that modifies the graph structure. Computing for each time step a new topology of the graph and applying centrality analysis based on PageRank, it is possible to identify the presence of new leaks at the water system. A confusion matrix evaluates the precision of the proposed methodology on defining where and when such leakages start and end. Seven leaks are used to validate the process, which presented 86% in accuracy terms. The results show the benefits of the method in terms of speed, computational efficiency, and precision in detecting leakages.
{"title":"Leak detection in water distribution networks based on graph signal processing of pressure data","authors":"Daniel Bezerra Barros, Rui Gabriel Souza, Gustavo Meirelles, Bruno Brentan","doi":"10.2166/hydro.2023.047","DOIUrl":"https://doi.org/10.2166/hydro.2023.047","url":null,"abstract":"Abstract Leakages in water distribution networks (WDNs) affect the hydraulic state of the entire or a large part of the network. Statistical correlation computed among pressure sensors monitoring network nodes aids the detection and localization of such leaks. This opens the possibility to work with water network databases, where graph signal processing (GSP) tools aid in understanding changes in pressure signals due to leakages in the hydraulic system. This paper presents a methodology for time-varying pressure signals on graph structures. The core of this methodology is based on changing of pressure, due to leaks, that modifies the graph structure. Computing for each time step a new topology of the graph and applying centrality analysis based on PageRank, it is possible to identify the presence of new leaks at the water system. A confusion matrix evaluates the precision of the proposed methodology on defining where and when such leakages start and end. Seven leaks are used to validate the process, which presented 86% in accuracy terms. The results show the benefits of the method in terms of speed, computational efficiency, and precision in detecting leakages.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136344090","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}
Maria Asucena Rodriguez-Ramirez, Óscar Arturo Fuentes-Mariles
Abstract The analysis of the spatial and temporal distribution of storm events contributes to a better use of water resources, for example, the supply of drinking water, irrigation practices, electricity generation and management of extreme events to control floods and mitigate droughts, among others. The traditional observation of rainfall fields in Mexico has been carried out using rain gauge network data, but their spatial representativeness is unsatisfactory. Therefore, this study reviewed the possibility of obtaining better estimates of the spatial distribution of daily rainfall considering information from three different databases, which include rain gauge measurements and remotely sensed precipitation products of satellite systems and weather radars. In order to determine a two-dimensional rainfall distribution, the information has been merged with a sequential data assimilation scheme up to the diagnostic stage, paying attention to the benefit that the rain gauge network density has on the estimation. With the application of the Barnes method, historical events in the Mexican territory were analyzed using statistical parameters for the validation of the estimates, with satisfactory results because the assimilated rainfalls turned out to be better approximations than the values calculated with the individual databases, even for a not very low density of surface observations.
{"title":"Daily rainfall assimilation based on satellite and weather radar precipitation products along with rain gauge networks","authors":"Maria Asucena Rodriguez-Ramirez, Óscar Arturo Fuentes-Mariles","doi":"10.2166/hydro.2023.104","DOIUrl":"https://doi.org/10.2166/hydro.2023.104","url":null,"abstract":"Abstract The analysis of the spatial and temporal distribution of storm events contributes to a better use of water resources, for example, the supply of drinking water, irrigation practices, electricity generation and management of extreme events to control floods and mitigate droughts, among others. The traditional observation of rainfall fields in Mexico has been carried out using rain gauge network data, but their spatial representativeness is unsatisfactory. Therefore, this study reviewed the possibility of obtaining better estimates of the spatial distribution of daily rainfall considering information from three different databases, which include rain gauge measurements and remotely sensed precipitation products of satellite systems and weather radars. In order to determine a two-dimensional rainfall distribution, the information has been merged with a sequential data assimilation scheme up to the diagnostic stage, paying attention to the benefit that the rain gauge network density has on the estimation. With the application of the Barnes method, historical events in the Mexican territory were analyzed using statistical parameters for the validation of the estimates, with satisfactory results because the assimilated rainfalls turned out to be better approximations than the values calculated with the individual databases, even for a not very low density of surface observations.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193449","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}
Abstract This study aims to explore the influence of various geometrical and hydraulic parameters on flow behavior and hydraulic conductivity in a single artificial fracture through a series of laboratory experiments. Laboratory experiments were conducted to examine unconfined groundwater flow through an artificially constructed single fracture. The fracture model consisted of varying aperture sizes (3, 9, and 12 mm) and different surface roughness conditions (fine, medium, and coarse sand coatings). Non-Darcian turbulent flow characteristics were observed at different flow rates, and the gradient of Reynolds number versus average flow velocity increased with aperture size. Flow parameters of the Darcian, Izbash, and Forchheimer models were calculated to characterize the flow behavior. Both the Forchheimer and Izbash models were found suitable for describing the non-Darcian flow characteristics under the prevailing conditions. The study revealed that hydraulic conductivity depended on flow length for fractures with different apertures and surface roughnesses, likely due to the presence of 2-D torturous flow within the rough fracture surface. These findings contribute to a better understanding of groundwater flow in fractured rock aquifers and provide valuable insights for modeling and managing such systems.
{"title":"Experimental study on non-Darcian flow through a single artificial fracture for different fracture apertures and surface roughness","authors":"Snigdha Pandey, Pramod Kumar Sharma","doi":"10.2166/hydro.2023.143","DOIUrl":"https://doi.org/10.2166/hydro.2023.143","url":null,"abstract":"Abstract This study aims to explore the influence of various geometrical and hydraulic parameters on flow behavior and hydraulic conductivity in a single artificial fracture through a series of laboratory experiments. Laboratory experiments were conducted to examine unconfined groundwater flow through an artificially constructed single fracture. The fracture model consisted of varying aperture sizes (3, 9, and 12 mm) and different surface roughness conditions (fine, medium, and coarse sand coatings). Non-Darcian turbulent flow characteristics were observed at different flow rates, and the gradient of Reynolds number versus average flow velocity increased with aperture size. Flow parameters of the Darcian, Izbash, and Forchheimer models were calculated to characterize the flow behavior. Both the Forchheimer and Izbash models were found suitable for describing the non-Darcian flow characteristics under the prevailing conditions. The study revealed that hydraulic conductivity depended on flow length for fractures with different apertures and surface roughnesses, likely due to the presence of 2-D torturous flow within the rough fracture surface. These findings contribute to a better understanding of groundwater flow in fractured rock aquifers and provide valuable insights for modeling and managing such systems.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135385598","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}
Francesco Gino Ciliberti, Luigi Berardi, Daniele Biagio Laucelli, Andres David Ariza, Laura Vanessa Enriquez, Orazio Giustolisi
Abstract In the context of water distribution networks (WDNs), researchers and technicians are actively working on new ways to transition into the digital era. They are focusing on creating standardized methods that fit the unique characteristics of these systems, with a strong emphasis on developing customized digital twins. This involves combining advanced hydraulic modeling with advanced data-driven techniques like artificial intelligence, machine learning, and deep learning. This paper begins by giving a detailed overview of the important progress that has led to this digital transformation. It highlights the potential to create interconnected digital water services (DWSs) that can support all aspects of managing, planning, and designing WDNs. This approach introduces standardized procedures that allow a continuous improvement of the digital representation of these networks. Additionally, technicians benefit from DWSs developed as QGIS software plugins. These services strategically enhance their understanding of technical decisions, improving logical reasoning, consistency, scalability, integrability, efficiency, effectiveness, and adaptability for both short-term and long-term management tasks. Notably, the framework remains adaptable, ready to embrace upcoming technological advancements and data gathering capabilities, all while keeping end-users central in shaping these technical developments.
{"title":"From digital twin paradigm to digital water services","authors":"Francesco Gino Ciliberti, Luigi Berardi, Daniele Biagio Laucelli, Andres David Ariza, Laura Vanessa Enriquez, Orazio Giustolisi","doi":"10.2166/hydro.2023.237","DOIUrl":"https://doi.org/10.2166/hydro.2023.237","url":null,"abstract":"Abstract In the context of water distribution networks (WDNs), researchers and technicians are actively working on new ways to transition into the digital era. They are focusing on creating standardized methods that fit the unique characteristics of these systems, with a strong emphasis on developing customized digital twins. This involves combining advanced hydraulic modeling with advanced data-driven techniques like artificial intelligence, machine learning, and deep learning. This paper begins by giving a detailed overview of the important progress that has led to this digital transformation. It highlights the potential to create interconnected digital water services (DWSs) that can support all aspects of managing, planning, and designing WDNs. This approach introduces standardized procedures that allow a continuous improvement of the digital representation of these networks. Additionally, technicians benefit from DWSs developed as QGIS software plugins. These services strategically enhance their understanding of technical decisions, improving logical reasoning, consistency, scalability, integrability, efficiency, effectiveness, and adaptability for both short-term and long-term management tasks. Notably, the framework remains adaptable, ready to embrace upcoming technological advancements and data gathering capabilities, all while keeping end-users central in shaping these technical developments.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136239731","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}
Antonietta Simone, Alessandra Cesaro, Cristiana Di Cristo, Oreste Fecarotta, Maria Cristina Morani
Abstract Monitoring of sewer networks (SNs) is an important task whose planning can be related to various purposes, for example contaminant detection and epidemiological studies. This paper proposes two different approaches for the identification of a monitoring system in SNs. The first one proposes the identification of the best monitoring points starting from the knowledge of the hydraulic behavior of the system with respect to specific sensor threshold values through an optimization procedure that maximizes the reliability in detecting a contaminant. A new mathematical model is developed and a global optimization solver is employed to perform the optimization procedure. The second approach is based on the complex network theory (CNT) tools, adopting the in-relevance-based harmonic centrality, and does not require any hydraulic simulation. The metric is evaluated for each node of the network and provides a range of nodes, classified with respect to their importance, useful to identify suitable locations for sensors. With reference to both a benchmark and a real SN, the comparison between the results achieved by both strategies indicates that the two approaches provide comparable solutions in terms of sensor location.
{"title":"Two different approaches for monitoring planning in sewer networks: topological vs. deterministic optimization","authors":"Antonietta Simone, Alessandra Cesaro, Cristiana Di Cristo, Oreste Fecarotta, Maria Cristina Morani","doi":"10.2166/hydro.2023.296","DOIUrl":"https://doi.org/10.2166/hydro.2023.296","url":null,"abstract":"Abstract Monitoring of sewer networks (SNs) is an important task whose planning can be related to various purposes, for example contaminant detection and epidemiological studies. This paper proposes two different approaches for the identification of a monitoring system in SNs. The first one proposes the identification of the best monitoring points starting from the knowledge of the hydraulic behavior of the system with respect to specific sensor threshold values through an optimization procedure that maximizes the reliability in detecting a contaminant. A new mathematical model is developed and a global optimization solver is employed to perform the optimization procedure. The second approach is based on the complex network theory (CNT) tools, adopting the in-relevance-based harmonic centrality, and does not require any hydraulic simulation. The metric is evaluated for each node of the network and provides a range of nodes, classified with respect to their importance, useful to identify suitable locations for sensors. With reference to both a benchmark and a real SN, the comparison between the results achieved by both strategies indicates that the two approaches provide comparable solutions in terms of sensor location.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312806","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}
Abstract Velocity distribution plays a fundamental role in understanding the hydrodynamics of open-channel flow. Among a multitude of approaches, the entropy-based approach holds great promise in achieving a reasonable characterisation of the velocity distribution. In entropy-based methods, the distribution depends on a key parameter, known as the entropy parameter (a function of the time-averaged mean velocity and maximum velocity), that relates to channel characteristics, such as channel roughness and channel bed slopes. The entropy parameter was regarded as constant for lack of experimental evidence, which would otherwise demonstrate if it had any correlation with channel properties. A series of experiments were conducted to collect velocity data in the laboratory flume for seven different values of the channel bed slope. The experimental data analysis revealed dissimilar fluctuations in entropy parameter values with varying bed slopes, with the lowest coefficient of variation in Renyi's (∼0.5%) and the highest in Shannon's case (∼10%). Performance evaluation of the predicted results substantiated good accuracy for all three entropies with the best results of Renyi entropy and lent strong support for using a constant (overall average) value of the entropy parameter for a specific channel cross-section rather than separate values for each channel bed slope.
{"title":"Influence of the channel bed slope on Shannon, Tsallis, and Renyi entropy parameters","authors":"Gurpinder Singh, Rakesh Khosa, Manoj Kumar Jain, Tommaso Moramarco, Vijay P. Singh","doi":"10.2166/hydro.2023.008","DOIUrl":"https://doi.org/10.2166/hydro.2023.008","url":null,"abstract":"Abstract Velocity distribution plays a fundamental role in understanding the hydrodynamics of open-channel flow. Among a multitude of approaches, the entropy-based approach holds great promise in achieving a reasonable characterisation of the velocity distribution. In entropy-based methods, the distribution depends on a key parameter, known as the entropy parameter (a function of the time-averaged mean velocity and maximum velocity), that relates to channel characteristics, such as channel roughness and channel bed slopes. The entropy parameter was regarded as constant for lack of experimental evidence, which would otherwise demonstrate if it had any correlation with channel properties. A series of experiments were conducted to collect velocity data in the laboratory flume for seven different values of the channel bed slope. The experimental data analysis revealed dissimilar fluctuations in entropy parameter values with varying bed slopes, with the lowest coefficient of variation in Renyi's (∼0.5%) and the highest in Shannon's case (∼10%). Performance evaluation of the predicted results substantiated good accuracy for all three entropies with the best results of Renyi entropy and lent strong support for using a constant (overall average) value of the entropy parameter for a specific channel cross-section rather than separate values for each channel bed slope.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395875","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}
Veysi Kartal, Muhammet Emin Emiroglu, Okan Mert Katipoglu, Erkan Karakoyun
Abstract Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863).
{"title":"Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques","authors":"Veysi Kartal, Muhammet Emin Emiroglu, Okan Mert Katipoglu, Erkan Karakoyun","doi":"10.2166/hydro.2023.230","DOIUrl":"https://doi.org/10.2166/hydro.2023.230","url":null,"abstract":"Abstract Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863).","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395162","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}
Honghong Zhang, Zhenwei Mu, Yiyun Wang, Zhen Zhou, Fan Fan, Fanqi Li, Hao Ma
Abstract Rough-strip energy dissipators (R-SEDs) can be arranged at the bend bottom of curved spillways to dissipate energy and divert flow for bend flow. Using the entropy weight and TOPSIS methods, a multi-criteria evaluation system was established for comprehensive energy dissipation and flow diversion effects of R-SEDs. Orthogonal tests and numerical simulation were conducted to analyze factors affecting these effects (average R-SED height, R-SED angle, R-SED spacing, bend width, bend centerline radius and discharge flow rate). It was found that bend width and bend centerline radius significantly affected R-SEDs' energy dissipation effects. Average R-SED height, R-SED spacing and bend centerline radius significantly affected R-SEDs' flow diversion effects. Bend width, average R-SED height and bend centerline radius significantly affected R-SEDs' combined effects of energy dissipation and flow diversion. Their energy dissipation effects were larger than the flow diversion effects. R-SEDs can effectively alleviate adverse hydraulic phenomena in curved spillways. With the recommended parameters, R-SEDs showed the best performance, with the energy dissipation rate increasing by 18.67% and the water surface superelevation coefficient decreasing by 26.14%. The accuracy of the multi-criteria evaluation system was verified. This study can provide a reference for the R-SED design of similar curved spillways.
{"title":"Study on the influencing parameters of rough-strip energy dissipators of curved spillways based on orthogonal tests and numerical simulation","authors":"Honghong Zhang, Zhenwei Mu, Yiyun Wang, Zhen Zhou, Fan Fan, Fanqi Li, Hao Ma","doi":"10.2166/hydro.2023.201","DOIUrl":"https://doi.org/10.2166/hydro.2023.201","url":null,"abstract":"Abstract Rough-strip energy dissipators (R-SEDs) can be arranged at the bend bottom of curved spillways to dissipate energy and divert flow for bend flow. Using the entropy weight and TOPSIS methods, a multi-criteria evaluation system was established for comprehensive energy dissipation and flow diversion effects of R-SEDs. Orthogonal tests and numerical simulation were conducted to analyze factors affecting these effects (average R-SED height, R-SED angle, R-SED spacing, bend width, bend centerline radius and discharge flow rate). It was found that bend width and bend centerline radius significantly affected R-SEDs' energy dissipation effects. Average R-SED height, R-SED spacing and bend centerline radius significantly affected R-SEDs' flow diversion effects. Bend width, average R-SED height and bend centerline radius significantly affected R-SEDs' combined effects of energy dissipation and flow diversion. Their energy dissipation effects were larger than the flow diversion effects. R-SEDs can effectively alleviate adverse hydraulic phenomena in curved spillways. With the recommended parameters, R-SEDs showed the best performance, with the energy dissipation rate increasing by 18.67% and the water surface superelevation coefficient decreasing by 26.14%. The accuracy of the multi-criteria evaluation system was verified. This study can provide a reference for the R-SED design of similar curved spillways.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782108","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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