Accurate runoff prediction is vital in optimizing reservoir scheduling, efficiently managing water resources, and ensuring the effective utilization of water resources. In this paper, a hybrid prediction model combining complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, CABES, and long short-term memory network (CEEMDAN-VMD-CABES-LSTM) is proposed. Firstly, CEEMDAN is used to decompose the original data, and the high-frequency component obtained from the CEEMDAN decomposition is decomposed using VMD. Then, each component is input into the LSTM optimized by CABES for prediction. Finally, the results of individual component predictions are combined and reconstructed to produce the monthly runoff predictions. The hybrid model is employed to predict the monthly runoff at the Xiajiang hydrological station and the Yingluoxia hydrological station. A comprehensive comparison is conducted with other models including BP, LSTM, SSA-LSTM, bald eagle search (BES)-LSTM, CABES-LSTM, CEEMDAN-CABES-LSTM, and VMD-CABES-LSTM. The assessment of each model's prediction performance uses four evaluation indexes. Results reveal that the CEEMDAN-VMD-CABES-LSTM model showcased the highest forecast accuracy among all the models evaluated. Compared with the single LSTM, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the Xiajiang hydrological station decreased by 71.09 and 65.26%, respectively, and the RMSE and MAPE of the Yingluoxia hydrological station decreased by 65.13 and 40.42%, respectively. The R and Nash efficiency coefficient (NSEC) values obtained for both sites are near 1.
{"title":"Improved monthly runoff time series prediction using the CABES-LSTM mixture model based on CEEMDAN-VMD decomposition","authors":"Dong-mei Xu, An-dong Liao, Wenchuan Wang, Wei-can Tian, Hong-fei Zang","doi":"10.2166/hydro.2023.216","DOIUrl":"https://doi.org/10.2166/hydro.2023.216","url":null,"abstract":"\u0000 \u0000 Accurate runoff prediction is vital in optimizing reservoir scheduling, efficiently managing water resources, and ensuring the effective utilization of water resources. In this paper, a hybrid prediction model combining complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, CABES, and long short-term memory network (CEEMDAN-VMD-CABES-LSTM) is proposed. Firstly, CEEMDAN is used to decompose the original data, and the high-frequency component obtained from the CEEMDAN decomposition is decomposed using VMD. Then, each component is input into the LSTM optimized by CABES for prediction. Finally, the results of individual component predictions are combined and reconstructed to produce the monthly runoff predictions. The hybrid model is employed to predict the monthly runoff at the Xiajiang hydrological station and the Yingluoxia hydrological station. A comprehensive comparison is conducted with other models including BP, LSTM, SSA-LSTM, bald eagle search (BES)-LSTM, CABES-LSTM, CEEMDAN-CABES-LSTM, and VMD-CABES-LSTM. The assessment of each model's prediction performance uses four evaluation indexes. Results reveal that the CEEMDAN-VMD-CABES-LSTM model showcased the highest forecast accuracy among all the models evaluated. Compared with the single LSTM, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the Xiajiang hydrological station decreased by 71.09 and 65.26%, respectively, and the RMSE and MAPE of the Yingluoxia hydrological station decreased by 65.13 and 40.42%, respectively. The R and Nash efficiency coefficient (NSEC) values obtained for both sites are near 1.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"12 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138979775","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}
Modeling of pressure-dependent users’ consumption is mandatory to simulate accurately the hydraulics of water distribution networks (WDNs). Several software solutions already exist for this purpose, but none of them actually permits the easy integration and test of new physical processes. In this paper, we propose a new Python simulator that implements a state-of-the-art pressure-dependent model (PDM) of users’ consumptions based on the Wagner’s pressure–outflow relationship (POR). We tested our simulator on eight large and complex WDNs, for different levels of users’ demands. The results show similar precision and efficiency as the ones obtained by the authors of the original model with their MATLAB implementation. Moreover, in case of fully satisfied users’ demands, our simulator provides same results as EPANET 2.0 in comparable computational times. Finally, our simulator is integrated into the open-source, collaborative, multi-platform, and Git versioned Python framework OOPNET (Object-Oriented Python framework for water distribution NETworks analyses); thus, it can be easily reused and/or extended by a large community of WDN modelers. All this work represents a preliminary step before the incorporation of new processes such as valves, pumps, and pressure-dependent background leakage outflows.
{"title":"A robust simulator of pressure-dependent consumption in Python","authors":"Camille Chambon, O. Piller, I. Mortazavi","doi":"10.2166/hydro.2023.218","DOIUrl":"https://doi.org/10.2166/hydro.2023.218","url":null,"abstract":"\u0000 \u0000 Modeling of pressure-dependent users’ consumption is mandatory to simulate accurately the hydraulics of water distribution networks (WDNs). Several software solutions already exist for this purpose, but none of them actually permits the easy integration and test of new physical processes. In this paper, we propose a new Python simulator that implements a state-of-the-art pressure-dependent model (PDM) of users’ consumptions based on the Wagner’s pressure–outflow relationship (POR). We tested our simulator on eight large and complex WDNs, for different levels of users’ demands. The results show similar precision and efficiency as the ones obtained by the authors of the original model with their MATLAB implementation. Moreover, in case of fully satisfied users’ demands, our simulator provides same results as EPANET 2.0 in comparable computational times. Finally, our simulator is integrated into the open-source, collaborative, multi-platform, and Git versioned Python framework OOPNET (Object-Oriented Python framework for water distribution NETworks analyses); thus, it can be easily reused and/or extended by a large community of WDN modelers. All this work represents a preliminary step before the incorporation of new processes such as valves, pumps, and pressure-dependent background leakage outflows.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"593 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138983095","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}
Javad Hatamiafkoueieh, Salim Heddam, Saeed Khoshtinat, Solmaz Khazaei, Abdol-Baset Osmani, Ebrahim Nohani, Mohammad Kiomarzi, Ehsan Sharafi, John Tiefenbacher
Abstract In this study, the vote algorithm used to improve the performances of three machine-learning models including M5Prime (M5P), random forest (RF), and random tree (RT) is developed (i.e. V-M5P, V-RF, and V-RT). Developed models were tested for forecasting soil temperature (TS) at 1, 2, and 3 days ahead at depths of 5 and 50 cm. All models were developed using different climatic variables, including mean, minimum, and maximum air temperatures; sunshine hours; evaporation; and solar radiation, which were evaluated. Correlation coefficients of 0.95 for the V-M5P model, 0.95 for the V-RF model, and 0.91 for the V-RT model were recorded for both 1- and 2-day ahead forecasting at a depth of 5 cm. For 3-day ahead forecasting, V-RF was the superior model with Nash–Sutcliff efficiency (NSE) values of 0.85, compared V-M5P's value of 0.81 and V-RT's value of 0.81. The results at a depth of 5 cm indicate that V-RT was the least effective model. At a depth of 50 cm, forecasted TsS was in good agreement with measurements, and the V-RF was slightly superior. Among the limitations of the current work is that the models were unable to improve their performances by increasing the forecasting horizon.
{"title":"Enhanced forecasting of multi-step ahead daily soil temperature using advanced hybrid vote algorithm-based tree models","authors":"Javad Hatamiafkoueieh, Salim Heddam, Saeed Khoshtinat, Solmaz Khazaei, Abdol-Baset Osmani, Ebrahim Nohani, Mohammad Kiomarzi, Ehsan Sharafi, John Tiefenbacher","doi":"10.2166/hydro.2023.188","DOIUrl":"https://doi.org/10.2166/hydro.2023.188","url":null,"abstract":"Abstract In this study, the vote algorithm used to improve the performances of three machine-learning models including M5Prime (M5P), random forest (RF), and random tree (RT) is developed (i.e. V-M5P, V-RF, and V-RT). Developed models were tested for forecasting soil temperature (TS) at 1, 2, and 3 days ahead at depths of 5 and 50 cm. All models were developed using different climatic variables, including mean, minimum, and maximum air temperatures; sunshine hours; evaporation; and solar radiation, which were evaluated. Correlation coefficients of 0.95 for the V-M5P model, 0.95 for the V-RF model, and 0.91 for the V-RT model were recorded for both 1- and 2-day ahead forecasting at a depth of 5 cm. For 3-day ahead forecasting, V-RF was the superior model with Nash–Sutcliff efficiency (NSE) values of 0.85, compared V-M5P's value of 0.81 and V-RT's value of 0.81. The results at a depth of 5 cm indicate that V-RT was the least effective model. At a depth of 50 cm, forecasted TsS was in good agreement with measurements, and the V-RF was slightly superior. Among the limitations of the current work is that the models were unable to improve their performances by increasing the forecasting horizon.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"31 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135041788","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 The accurate prediction of maximum erosion depth in riverbeds is crucial for early protection of bank slopes. In this study, K-means clustering analysis was used for outlier identification and feature selection, resulting in Plan 1 with six influential features. Plan 2 included features selected by existing methods. Regression models were built using Support Vector Regression, Random Forest Regression (RF Regression), and eXtreme Gradient Boosting on sample data from Plan 1 and Plan 2. To enhance accuracy, a Stacking method with a feed-forward neural network was introduced as the meta-learner. Model performance was evaluated using root mean squared error, mean absolute error, mean absolute percentage error, and R2 coefficients. The results demonstrate that the performance of the three models in Plan 1 outperformed that of Plan 2, with improvements in R2 values of 0.0025, 0.0423, and 0.0205, respectively. Among the three regression models in Plan 1, RF Regression performs the best with an R2 value of 0.9149 but still lower than the 0.9389 achieved by the Stacking fusion model. Compared to the existing formulas, the Stacking model exhibits superior predictive performance. This study verifies the effectiveness of combining clustering analysis, feature selection, and the Stacking method in predicting maximum scour depth in bends, providing a novel approach for bank protection design.
{"title":"Prediction of maximum scour depth in river bends by the Stacking model","authors":"Junfeng Chen, Xiaoquan Zhou, Lirong Xiao, Yuhang Huang","doi":"10.2166/hydro.2023.177","DOIUrl":"https://doi.org/10.2166/hydro.2023.177","url":null,"abstract":"Abstract The accurate prediction of maximum erosion depth in riverbeds is crucial for early protection of bank slopes. In this study, K-means clustering analysis was used for outlier identification and feature selection, resulting in Plan 1 with six influential features. Plan 2 included features selected by existing methods. Regression models were built using Support Vector Regression, Random Forest Regression (RF Regression), and eXtreme Gradient Boosting on sample data from Plan 1 and Plan 2. To enhance accuracy, a Stacking method with a feed-forward neural network was introduced as the meta-learner. Model performance was evaluated using root mean squared error, mean absolute error, mean absolute percentage error, and R2 coefficients. The results demonstrate that the performance of the three models in Plan 1 outperformed that of Plan 2, with improvements in R2 values of 0.0025, 0.0423, and 0.0205, respectively. Among the three regression models in Plan 1, RF Regression performs the best with an R2 value of 0.9149 but still lower than the 0.9389 achieved by the Stacking fusion model. Compared to the existing formulas, the Stacking model exhibits superior predictive performance. This study verifies the effectiveness of combining clustering analysis, feature selection, and the Stacking method in predicting maximum scour depth in bends, providing a novel approach for bank protection design.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"106 39","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136521","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}
Giang Song Le, Long Thanh Tran, Loc Huu Ho, Edward Park
Abstract Coupling models of different dimensions is one of the most important yet under-represented challenges. This paper introduces a new modeling strategy to streamline a more flexible and effective integrated one-dimensional (1D)/two-dimensional (2D) model for floodplains along lowland rivers. The 1D model, utilizing the finite volume method, solves the Saint–Venant equations, while the 2D mesh employs unstructured quadrilateral elements. The two strategies couple the 1D/2D models: direct 1D/2D connection by the law of mass conservation at supernode, and lateral 1D/2D model connection by spillways at riverbank. The coupling strategy in F28 guarantees the water balance and the conservation of momentum at the integrated 1D/2D nodes. The model was applied to the Mekong Delta to address the capacity of hydrodynamic simulations integrating various water infrastructures. Results showed that the developed model has a strong potential to be applied to other lowland rivers worldwide with complex infrastructures.
{"title":"F28: a novel coupling strategy for 1D/2D hydraulic models for flood risk assessment of the Mekong Delta","authors":"Giang Song Le, Long Thanh Tran, Loc Huu Ho, Edward Park","doi":"10.2166/hydro.2023.108","DOIUrl":"https://doi.org/10.2166/hydro.2023.108","url":null,"abstract":"Abstract Coupling models of different dimensions is one of the most important yet under-represented challenges. This paper introduces a new modeling strategy to streamline a more flexible and effective integrated one-dimensional (1D)/two-dimensional (2D) model for floodplains along lowland rivers. The 1D model, utilizing the finite volume method, solves the Saint–Venant equations, while the 2D mesh employs unstructured quadrilateral elements. The two strategies couple the 1D/2D models: direct 1D/2D connection by the law of mass conservation at supernode, and lateral 1D/2D model connection by spillways at riverbank. The coupling strategy in F28 guarantees the water balance and the conservation of momentum at the integrated 1D/2D nodes. The model was applied to the Mekong Delta to address the capacity of hydrodynamic simulations integrating various water infrastructures. Results showed that the developed model has a strong potential to be applied to other lowland rivers worldwide with complex infrastructures.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" 41","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135291546","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}
Mingyan Wang, Paguédame Game, Philippe Gourbesville
Abstract To achieve an integrated river basin management for the Cagne catchment (France) and better predict the flood, various modeling tools are integrated within a unified framework, forming a decision support system (DSS). In the paper, an integrated modeling approach employing deterministic distributed hydrological (MIKE SHE), hydraulic (MIKE 21 FM), and hydrogeological (FEFLOW) models is presented. The hydrological model was validated with recorded data and following a sensitivity analysis for optimizing grid resolution with 20 m. The hydraulic model based on MIKE 21 FM utilizes the results generated by the MIKE SHE model as boundary conditions, producing inundation maps for both normal and extreme periods. The hydrogeological model addresses the various complex relationships taking place within the catchment and was validated with piezometer data. The integration of these three models into a DSS provides a valuable tool for decision-makers to manage the Cagne catchment and the water-related issues more effectively during various hydrological situations. This comprehensive modeling framework underscores the importance of interdisciplinary approaches for addressing complex hydrological processes and contributes to improved flood management strategies in the catchment.
{"title":"Advancing integrated river basin management and flood forecasting in the Cagne catchment: a combined approach using deterministic distributed models","authors":"Mingyan Wang, Paguédame Game, Philippe Gourbesville","doi":"10.2166/hydro.2023.100","DOIUrl":"https://doi.org/10.2166/hydro.2023.100","url":null,"abstract":"Abstract To achieve an integrated river basin management for the Cagne catchment (France) and better predict the flood, various modeling tools are integrated within a unified framework, forming a decision support system (DSS). In the paper, an integrated modeling approach employing deterministic distributed hydrological (MIKE SHE), hydraulic (MIKE 21 FM), and hydrogeological (FEFLOW) models is presented. The hydrological model was validated with recorded data and following a sensitivity analysis for optimizing grid resolution with 20 m. The hydraulic model based on MIKE 21 FM utilizes the results generated by the MIKE SHE model as boundary conditions, producing inundation maps for both normal and extreme periods. The hydrogeological model addresses the various complex relationships taking place within the catchment and was validated with piezometer data. The integration of these three models into a DSS provides a valuable tool for decision-makers to manage the Cagne catchment and the water-related issues more effectively during various hydrological situations. This comprehensive modeling framework underscores the importance of interdisciplinary approaches for addressing complex hydrological processes and contributes to improved flood management strategies in the catchment.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"151 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480473","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}
Nuno M. C. Martins, Dídia I. C. Covas, Silvia Meniconi, Caterina Capponi, Bruno Brunone
Abstract In this paper, an advanced three-dimensional (3D) computational fluid dynamics (CFD) model is used to analyse the steady-state hydrodynamics of laminar flow through an extended partial blockage (PB) in a pressurised pipe. PB corresponds to one of the main faults affecting pipelines. In fact, it reduces its carrying capacity with economic consequences, and as it does not give rise to any external evidence, its detection can be very challenging. The performance of the model is evaluated by comparing the numerical results with the available experimental data from the literature. Subsequently, the velocity and pressure distributions are analysed, and the main features of the flow field are described in terms of both local and global dimensionless parameters. Furthermore, the behaviour of the discharge coefficient is also investigated. The obtained results confirm that steady-state measurements can identify the presence of PB and follow its evolution over time but cannot detect its location and size. On the other hand, the location and severity of PBs can be provided by means of transient tests.
{"title":"Hydrodynamics of laminar pipe flow through an extended partial blockage by CFD","authors":"Nuno M. C. Martins, Dídia I. C. Covas, Silvia Meniconi, Caterina Capponi, Bruno Brunone","doi":"10.2166/hydro.2023.042","DOIUrl":"https://doi.org/10.2166/hydro.2023.042","url":null,"abstract":"Abstract In this paper, an advanced three-dimensional (3D) computational fluid dynamics (CFD) model is used to analyse the steady-state hydrodynamics of laminar flow through an extended partial blockage (PB) in a pressurised pipe. PB corresponds to one of the main faults affecting pipelines. In fact, it reduces its carrying capacity with economic consequences, and as it does not give rise to any external evidence, its detection can be very challenging. The performance of the model is evaluated by comparing the numerical results with the available experimental data from the literature. Subsequently, the velocity and pressure distributions are analysed, and the main features of the flow field are described in terms of both local and global dimensionless parameters. Furthermore, the behaviour of the discharge coefficient is also investigated. The obtained results confirm that steady-state measurements can identify the presence of PB and follow its evolution over time but cannot detect its location and size. On the other hand, the location and severity of PBs can be provided by means of transient tests.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"12 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135873769","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 presents the development of a novel hybrid wind power generator–water distillation system with the objective of providing sustainable solutions for impoverished isolated communities facing limited resources. The advantage of the proposed system is its ability to operate day and night; therefore, it produces larger quantities of distilled water even on cloudy days with winds. The system comprises a Venturi tunnel integrated with a wind turbine, an attached impure water tank, and a condenser located at the end section. The accelerated airflow at the throat section serves two purposes: water evaporation from the tank and power generation through the wind turbine. The evaporated water is subsequently collected as the airflow decelerates and the pressure decreases along the diverging section. Theoretical and computational modelling is employed to design the system by examining air speed, area ratio, relative humidity, as well as air, and water temperatures. The system exhibits enhanced performance under warm and dry weather conditions, thereby optimizing its performance. Conversely, temperature and relative humidity do not affect power generation; it was increased by higher air speeds and larger area ratios. This data-driven approach ensures optimal design parameters are selected, aligning the system's capabilities with the specific freshwater demand.
{"title":"Modelling of a hybrid wind power generator–water distillation system using a Venturi tunnel","authors":"Malak I. Naji, M. A. Al-Nimr","doi":"10.2166/hydro.2023.269","DOIUrl":"https://doi.org/10.2166/hydro.2023.269","url":null,"abstract":"Abstract This study presents the development of a novel hybrid wind power generator–water distillation system with the objective of providing sustainable solutions for impoverished isolated communities facing limited resources. The advantage of the proposed system is its ability to operate day and night; therefore, it produces larger quantities of distilled water even on cloudy days with winds. The system comprises a Venturi tunnel integrated with a wind turbine, an attached impure water tank, and a condenser located at the end section. The accelerated airflow at the throat section serves two purposes: water evaporation from the tank and power generation through the wind turbine. The evaporated water is subsequently collected as the airflow decelerates and the pressure decreases along the diverging section. Theoretical and computational modelling is employed to design the system by examining air speed, area ratio, relative humidity, as well as air, and water temperatures. The system exhibits enhanced performance under warm and dry weather conditions, thereby optimizing its performance. Conversely, temperature and relative humidity do not affect power generation; it was increased by higher air speeds and larger area ratios. This data-driven approach ensures optimal design parameters are selected, aligning the system's capabilities with the specific freshwater demand.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"20 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933094","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}
Drinking water utilities and commercial vendors are developing battery-powered autonomous robots for the internal inspection of pipelines. However, these robots require nearby charging stations next to the pipelines of the water distribution networks (WDN). This prompts practical questions about the minimal number of charging stations and robots required. To address the questions, an integer linear programming optimization is formulated, akin to set covering, based on the shortest path of the charging stations to each node along a pipeline. The optimization decisions revolve around designating nodes as charging stations, considering the maximum distance (δmax) at which a robot can cover a hard constraint. For optimal placement, two objective formulations are proposed: (i) minimize the total number of stations, representing total cost; and (ii) maximize the total redundancy of the system. The methodology is applied to three WDN topologies (i.e. Modena, Five Reservoirs, and E−Town). Results show the influence of topology on the total number of stations, the number of robots, and the redundancy of the charging stations network. A trade-off between δmax and total number of stations emphasizes robot battery capacity's significance mariocastrogama.
{"title":"Optimal charging station placement for autonomous robots in drinking water networks","authors":"Mario Castro-Gama, Yvonne Hassink-Mulder","doi":"10.2166/hydro.2023.040","DOIUrl":"https://doi.org/10.2166/hydro.2023.040","url":null,"abstract":"Drinking water utilities and commercial vendors are developing battery-powered autonomous robots for the internal inspection of pipelines. However, these robots require nearby charging stations next to the pipelines of the water distribution networks (WDN). This prompts practical questions about the minimal number of charging stations and robots required. To address the questions, an integer linear programming optimization is formulated, akin to set covering, based on the shortest path of the charging stations to each node along a pipeline. The optimization decisions revolve around designating nodes as charging stations, considering the maximum distance (δmax) at which a robot can cover a hard constraint. For optimal placement, two objective formulations are proposed: (i) minimize the total number of stations, representing total cost; and (ii) maximize the total redundancy of the system. The methodology is applied to three WDN topologies (i.e. Modena, Five Reservoirs, and E−Town). Results show the influence of topology on the total number of stations, the number of robots, and the redundancy of the charging stations network. A trade-off between δmax and total number of stations emphasizes robot battery capacity's significance mariocastrogama.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"9 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973331","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}
Changli Li, Zheng Han, Yange Li, Ming Li, Weidong Wang, Ningsheng Chen, Guisheng Hu
Abstract The computational prediction of wave propagation in dam-break floods is a long-standing problem in hydrodynamics and hydrology. We show that a reservoir computing echo state network (RC-ESN) that is well-trained on a minimal amount of data can accurately predict the long-term dynamic behavior of a one-dimensional dam-break flood. We solve the de Saint-Venant equations for a one-dimensional dam-break flood scenario using the Lax–Wendroff numerical scheme and train the RC-ESN model. The results demonstrate that the RC-ESN model has good prediction ability, as it predicts wave propagation behavior 286 time-steps ahead with a root mean square error smaller than 0.01, outperforming the conventional long short-term memory (LSTM) model, which only predicts 81 time-steps ahead. We also provide a sensitivity analysis of prediction accuracy for RC-ESN's key parameters such as training set size, reservoir size, and spectral radius. Results indicate that the RC-ESN is less dependent on training set size, with a medium reservoir size of 1,200–2,600 sufficient. We confirm that the spectral radius has a complex influence on the prediction accuracy and currently recommend a smaller spectral radius. Even when the initial flow depth of the dam break is changed, the prediction horizon of RC-ESN remains greater than that of LSTM.
{"title":"Data-driven and echo state network-based prediction of wave propagation behavior in dam-break flood","authors":"Changli Li, Zheng Han, Yange Li, Ming Li, Weidong Wang, Ningsheng Chen, Guisheng Hu","doi":"10.2166/hydro.2023.035","DOIUrl":"https://doi.org/10.2166/hydro.2023.035","url":null,"abstract":"Abstract The computational prediction of wave propagation in dam-break floods is a long-standing problem in hydrodynamics and hydrology. We show that a reservoir computing echo state network (RC-ESN) that is well-trained on a minimal amount of data can accurately predict the long-term dynamic behavior of a one-dimensional dam-break flood. We solve the de Saint-Venant equations for a one-dimensional dam-break flood scenario using the Lax–Wendroff numerical scheme and train the RC-ESN model. The results demonstrate that the RC-ESN model has good prediction ability, as it predicts wave propagation behavior 286 time-steps ahead with a root mean square error smaller than 0.01, outperforming the conventional long short-term memory (LSTM) model, which only predicts 81 time-steps ahead. We also provide a sensitivity analysis of prediction accuracy for RC-ESN's key parameters such as training set size, reservoir size, and spectral radius. Results indicate that the RC-ESN is less dependent on training set size, with a medium reservoir size of 1,200–2,600 sufficient. We confirm that the spectral radius has a complex influence on the prediction accuracy and currently recommend a smaller spectral radius. Even when the initial flow depth of the dam break is changed, the prediction horizon of RC-ESN remains greater than that of LSTM.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"14 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135932824","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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