An improved understanding of bioretention cell (BC) design configuration at both the unit scale and catchment scale is necessary for critical insight into dynamical behaviors of design parameters which resultantly guides and improves the effectiveness and efficiency of a BC. A comprehensive sensitivity analysis (SA) of BC design parameters was conducted in this study by using the Stormwater Management Model (SWMM) which is globally used for BC's modeling. The preliminary screening of various design parameters is conducted by the one-factor-at-a-time (OAT) SA method and the key influential parameters (i.e., conductivity, berm height, vegetation volume, suction head, porosity, wilting point, and soil thickness) are selected for further SA. To this end, 1,000 random uniformly distributed samples of each sensitive design parameter are simulated by a Python wrapper of SWMM (PySWMM) under different design storms at the unit scale and catchment scale, respectively. Unit-scale SA results found unique characteristics of each design parameter under different storm scenarios, and their behaviors toward different model responses dynamically change within their factor spaces. Catchment-scale SA results conclude vegetation and soil layers design parameters have significant impacts on controlling stormwater at the catchment scale, and optimal selection of design parameters of vegetation (type, density, and height) and soil (type, layer thickness, and void ratio) is necessary for significantly improving the effectiveness of the BC at the catchment scale.
{"title":"Unit-scale- and catchment-scale-based sensitivity analysis of bioretention cell for urban stormwater system management","authors":"Husnain Tansar, H. Duan, O. Mark","doi":"10.2166/hydro.2023.049","DOIUrl":"https://doi.org/10.2166/hydro.2023.049","url":null,"abstract":"\u0000 \u0000 An improved understanding of bioretention cell (BC) design configuration at both the unit scale and catchment scale is necessary for critical insight into dynamical behaviors of design parameters which resultantly guides and improves the effectiveness and efficiency of a BC. A comprehensive sensitivity analysis (SA) of BC design parameters was conducted in this study by using the Stormwater Management Model (SWMM) which is globally used for BC's modeling. The preliminary screening of various design parameters is conducted by the one-factor-at-a-time (OAT) SA method and the key influential parameters (i.e., conductivity, berm height, vegetation volume, suction head, porosity, wilting point, and soil thickness) are selected for further SA. To this end, 1,000 random uniformly distributed samples of each sensitive design parameter are simulated by a Python wrapper of SWMM (PySWMM) under different design storms at the unit scale and catchment scale, respectively. Unit-scale SA results found unique characteristics of each design parameter under different storm scenarios, and their behaviors toward different model responses dynamically change within their factor spaces. Catchment-scale SA results conclude vegetation and soil layers design parameters have significant impacts on controlling stormwater at the catchment scale, and optimal selection of design parameters of vegetation (type, density, and height) and soil (type, layer thickness, and void ratio) is necessary for significantly improving the effectiveness of the BC at the catchment scale.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47490105","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}
M. Hajibabaei, Sina Hesarkazzazi, Amin Minaei, D. Savić, R. Sitzenfrei
One of the main drawbacks of using evolutionary algorithms for the multi-objective design of water distribution networks (WDNs) is their computational inefficiency, particularly for large-scale problems. Recently, graph theory-based approaches (GTAs) have gained attention as they can help with the optimal WDN design (i.e., determining optimal diameters). This study aims to extend a GTA to further improve the quality of design solutions. The GTA design is based on a customized metric called ‘demand edge betweenness centrality’, which spatially distributes nodal demands through the weighted edges of a WDN graph and provides an estimation of water flows. Assigned edge weights can be constant (i.e., static) or modified iteratively (i.e., dynamic) during the design process, leading to different flow estimations and alternative design options. Three hydraulic-inspired dynamic weights are developed in this study to better reproduce hydraulic behavior and, consequently, find better solutions. Additionally, this work proposes a framework for the optimal design of multi-source WDNs and provides guidelines for obtaining near-optimal solutions in such networks. A comparative study between GTAs and evolutionary optimizations confirms the efficiency of the improved GTA in providing optimal/near-optimal solutions, especially for large WDNs, with a runtime reduction of up to seven orders of magnitude.
{"title":"Pareto-optimal design of water distribution networks: an improved graph theory-based approach","authors":"M. Hajibabaei, Sina Hesarkazzazi, Amin Minaei, D. Savić, R. Sitzenfrei","doi":"10.2166/hydro.2023.091","DOIUrl":"https://doi.org/10.2166/hydro.2023.091","url":null,"abstract":"\u0000 \u0000 One of the main drawbacks of using evolutionary algorithms for the multi-objective design of water distribution networks (WDNs) is their computational inefficiency, particularly for large-scale problems. Recently, graph theory-based approaches (GTAs) have gained attention as they can help with the optimal WDN design (i.e., determining optimal diameters). This study aims to extend a GTA to further improve the quality of design solutions. The GTA design is based on a customized metric called ‘demand edge betweenness centrality’, which spatially distributes nodal demands through the weighted edges of a WDN graph and provides an estimation of water flows. Assigned edge weights can be constant (i.e., static) or modified iteratively (i.e., dynamic) during the design process, leading to different flow estimations and alternative design options. Three hydraulic-inspired dynamic weights are developed in this study to better reproduce hydraulic behavior and, consequently, find better solutions. Additionally, this work proposes a framework for the optimal design of multi-source WDNs and provides guidelines for obtaining near-optimal solutions in such networks. A comparative study between GTAs and evolutionary optimizations confirms the efficiency of the improved GTA in providing optimal/near-optimal solutions, especially for large WDNs, with a runtime reduction of up to seven orders of magnitude.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48872082","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}
The chlorine and total trihalomethane (TTHM) concentrations are sparsely measured in the trunk network of Bogotá, Colombia, which leads to a high uncertainty level at an operational level. For this reason, this research assessed the prediction accuracy for chlorine and TTHM concentrations of two black-box models based on the following artificial intelligence techniques: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) as a modelling alternative. The simulation results of a hydraulic and water quality analysis of the network in EPANET and its multi-species extension EPANET-MSX were used for training the black-box models. Subsequently, the Threat Ensemble Vulnerability Assessment-Sensor Placement Optimization Tool (TEVA-SPOT) and Evolutionary Polynomial Regression-Multi-Objective Genetic Algorithm (EPR-MOGA-XL) were jointly applied to select the most representative input variables and locations for predicting water quality at other points of the network. ANNs and ANFIS were optimized with a multi-objective approach to reach a compromise between training performance and generalization capacity. The ANFIS models had a higher mean Training and Test Nash–Sutcliffe Index (NSI) in contrast with ANNs. In general, the models had a satisfactory mean prediction performance. However, some of them did not achieve suitable Test NSI values, and the prediction accuracy for different operational statuses was limited.
{"title":"Application of black-box models based on artificial intelligence for the prediction of chlorine and TTHMs in the trunk network of Bogotá, Colombia","authors":"Laura Enríquez, Laura González, J. Saldarriaga","doi":"10.2166/hydro.2023.028","DOIUrl":"https://doi.org/10.2166/hydro.2023.028","url":null,"abstract":"\u0000 \u0000 The chlorine and total trihalomethane (TTHM) concentrations are sparsely measured in the trunk network of Bogotá, Colombia, which leads to a high uncertainty level at an operational level. For this reason, this research assessed the prediction accuracy for chlorine and TTHM concentrations of two black-box models based on the following artificial intelligence techniques: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) as a modelling alternative. The simulation results of a hydraulic and water quality analysis of the network in EPANET and its multi-species extension EPANET-MSX were used for training the black-box models. Subsequently, the Threat Ensemble Vulnerability Assessment-Sensor Placement Optimization Tool (TEVA-SPOT) and Evolutionary Polynomial Regression-Multi-Objective Genetic Algorithm (EPR-MOGA-XL) were jointly applied to select the most representative input variables and locations for predicting water quality at other points of the network. ANNs and ANFIS were optimized with a multi-objective approach to reach a compromise between training performance and generalization capacity. The ANFIS models had a higher mean Training and Test Nash–Sutcliffe Index (NSI) in contrast with ANNs. In general, the models had a satisfactory mean prediction performance. However, some of them did not achieve suitable Test NSI values, and the prediction accuracy for different operational statuses was limited.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"1 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67849104","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}
Artificial neural networks (ANNs) are labeled as black-box techniques which limit their operational uses in hydrology. Recently, researchers explored techniques that provide insight into the various elements of ANN and their relationship with the physical components of the system being modeled which are commonly known as knowledge extraction (KE) techniques. However, the physical components of rainfall-runoff (RR) process utilized in these KE techniques are obtained from primitive baseflow separation techniques without considering other components of RR process utilizing mostly base flow and surface flow till now. To identify if ANN acquires physical components of RR process, a well-established water balance model (Australian Water Balance Model) has been utilized first time in this study. For this purpose, correlation and visualization techniques have been used for the Kentucky River basin, USA. Results show that ANN architecture having a single hidden layer with four hidden neurons was the best in simulating RR process and each of the four hidden neurons corresponds to certain subprocesses of the overall RR process, i.e., two hidden neurons are capturing surface flow dynamics with lower and higher flows, one is capturing base flow dynamics, and last one is having good relations with past rainfalls showing that ANN captures physics of basin's RR process.
{"title":"Does ANN really acquire the physics of the system? A study using conceptual components from an established water balance model","authors":"Vikas Kumar Vidyarthi, Ashu Jain","doi":"10.2166/hydro.2023.025","DOIUrl":"https://doi.org/10.2166/hydro.2023.025","url":null,"abstract":"\u0000 \u0000 Artificial neural networks (ANNs) are labeled as black-box techniques which limit their operational uses in hydrology. Recently, researchers explored techniques that provide insight into the various elements of ANN and their relationship with the physical components of the system being modeled which are commonly known as knowledge extraction (KE) techniques. However, the physical components of rainfall-runoff (RR) process utilized in these KE techniques are obtained from primitive baseflow separation techniques without considering other components of RR process utilizing mostly base flow and surface flow till now. To identify if ANN acquires physical components of RR process, a well-established water balance model (Australian Water Balance Model) has been utilized first time in this study. For this purpose, correlation and visualization techniques have been used for the Kentucky River basin, USA. Results show that ANN architecture having a single hidden layer with four hidden neurons was the best in simulating RR process and each of the four hidden neurons corresponds to certain subprocesses of the overall RR process, i.e., two hidden neurons are capturing surface flow dynamics with lower and higher flows, one is capturing base flow dynamics, and last one is having good relations with past rainfalls showing that ANN captures physics of basin's RR process.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44231464","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}
We introduce HydroLang Markup Language (HL-ML), a programming interface that uses a markup language to perform environmental analyses using the hydrological and environmental framework HydroLang. The software acts as a self-contained interface that uses HTML tags powered by the web component specification to generate simple hydrological computations that enable data analysis, visualization, and manipulation via semantically driven instructions. It enables hydrological researchers and professionals to use markup language to retrieve, analyze, visualize, and map data with basic programming skills. The components' adaptability enables users to run analytical routines that perform simple and complex analyses on the client side. We present the implementation details of the approach, the use of custom elements in web technologies and academia, and share sample usages to demonstrate the simplicity of use of the human-readable and computer-executable framework.
{"title":"HydroLang Markup Language: community-driven web components for hydrological analyses","authors":"Carlos Erazo Ramirez, Y. Sermet, I. Demir","doi":"10.2166/hydro.2023.149","DOIUrl":"https://doi.org/10.2166/hydro.2023.149","url":null,"abstract":"\u0000 \u0000 We introduce HydroLang Markup Language (HL-ML), a programming interface that uses a markup language to perform environmental analyses using the hydrological and environmental framework HydroLang. The software acts as a self-contained interface that uses HTML tags powered by the web component specification to generate simple hydrological computations that enable data analysis, visualization, and manipulation via semantically driven instructions. It enables hydrological researchers and professionals to use markup language to retrieve, analyze, visualize, and map data with basic programming skills. The components' adaptability enables users to run analytical routines that perform simple and complex analyses on the client side. We present the implementation details of the approach, the use of custom elements in web technologies and academia, and share sample usages to demonstrate the simplicity of use of the human-readable and computer-executable framework.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43685706","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}
The assessment of visual blockages in cross-drainage hydraulic structures, such as culverts and bridges, is crucial for ensuring their efficient functioning and preventing flash flooding incidents. The extraction of blockage-related information through computer vision algorithms can provide valuable insights into the visual blockage. However, the absence of comprehensive datasets has posed a significant challenge in effectively training computer vision models. In this study, we explore the use of synthetic data in combination with a limited real-world dataset, the images of culvert openings and blockage (ICOB), to evaluate the performance of a culvert opening detector. The Faster R-CNN model with a ResNet50 backbone was used as the culvert opening detector. The impact of synthetic data was evaluated through two experiments. The first involved training the model with different combinations of synthetic and real-world data, while the second involved training the model with reduced real-world images. The results of the first experiment revealed that structured training, where the synthetic images of culvert (SIC) were used for initial training and the ICOB was used for fine-tuning, resulted in slightly improved detection performance. The second experiment showed that the use of synthetic data, in conjunction with a reduced number of real-world images, resulted in significantly improved degradation rates.
{"title":"Visual blockage assessment at culverts using synthetic images to mitigate blockage-originated floods","authors":"Umair Iqbal, J. Barthélemy, Pascal Perez","doi":"10.2166/hydro.2023.068","DOIUrl":"https://doi.org/10.2166/hydro.2023.068","url":null,"abstract":"\u0000 The assessment of visual blockages in cross-drainage hydraulic structures, such as culverts and bridges, is crucial for ensuring their efficient functioning and preventing flash flooding incidents. The extraction of blockage-related information through computer vision algorithms can provide valuable insights into the visual blockage. However, the absence of comprehensive datasets has posed a significant challenge in effectively training computer vision models. In this study, we explore the use of synthetic data in combination with a limited real-world dataset, the images of culvert openings and blockage (ICOB), to evaluate the performance of a culvert opening detector. The Faster R-CNN model with a ResNet50 backbone was used as the culvert opening detector. The impact of synthetic data was evaluated through two experiments. The first involved training the model with different combinations of synthetic and real-world data, while the second involved training the model with reduced real-world images. The results of the first experiment revealed that structured training, where the synthetic images of culvert (SIC) were used for initial training and the ICOB was used for fine-tuning, resulted in slightly improved detection performance. The second experiment showed that the use of synthetic data, in conjunction with a reduced number of real-world images, resulted in significantly improved degradation rates.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45601362","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}
This paper presents a new framework for modeling the bargaining process among stakeholders by coupling social choice and bargaining methods. Based on this framework, two methods of evolutionary bargaining coupled with Borda count (BBC) and evolutionary bargaining coupled with pairwise voting (BPV) are proposed, and the results of applying them as challenging problems of allocating water and reclaimed wastewater in agricultural regions are analyzed. After proposing some candidate scenarios of allocating water and reclaimed wastewater, non-dominated scenarios are determined. Then, in the first level of bargaining, using a social choice technique, each stakeholder chooses the most desirable scenario out of the non-dominated ones, regardless of the utilities of other stakeholders. The selected scenarios by all stakeholders can provide them an estimate of other stakeholders' expected utilities. This enables each stakeholder in the next step of bargaining to suggest a scenario that improves their own utility and gives the minimum acceptable utility of other stakeholders. If the bargaining process provides more than one scenario, a social choice method is applied to find the most preferred scenario. The applicability and performance of the proposed framework are evaluated by applying it to the Varamin plain, in the south-east of Tehran, Iran.
{"title":"An evolutionary bargaining framework for allocating water and reclaimed wastewater in agricultural regions","authors":"M. Hosseini, N. Mahjouri, Niloofar Farsi","doi":"10.2166/hydro.2023.112","DOIUrl":"https://doi.org/10.2166/hydro.2023.112","url":null,"abstract":"\u0000 This paper presents a new framework for modeling the bargaining process among stakeholders by coupling social choice and bargaining methods. Based on this framework, two methods of evolutionary bargaining coupled with Borda count (BBC) and evolutionary bargaining coupled with pairwise voting (BPV) are proposed, and the results of applying them as challenging problems of allocating water and reclaimed wastewater in agricultural regions are analyzed. After proposing some candidate scenarios of allocating water and reclaimed wastewater, non-dominated scenarios are determined. Then, in the first level of bargaining, using a social choice technique, each stakeholder chooses the most desirable scenario out of the non-dominated ones, regardless of the utilities of other stakeholders. The selected scenarios by all stakeholders can provide them an estimate of other stakeholders' expected utilities. This enables each stakeholder in the next step of bargaining to suggest a scenario that improves their own utility and gives the minimum acceptable utility of other stakeholders. If the bargaining process provides more than one scenario, a social choice method is applied to find the most preferred scenario. The applicability and performance of the proposed framework are evaluated by applying it to the Varamin plain, in the south-east of Tehran, Iran.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46242974","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}
Ensuring an optimal irrigation system and planting layout for crops in areas with water resource deficiencies is a complex process. A model of the optimal allocation of water and land resources for the irrigation system of the ‘reservoir and pumping station’ under crop rotation was established in this study. For the above complex nonlinear model, two-hybrid algorithms are proposed: (1) the decomposition aggregation dynamic programming (DADP) method and linear programming (LP) successive approximation algorithm [(DADP–LP)SA] and (2) the DADP algorithm based on the orthogonal design (OD) method (OD–DADP). The (DADP–LP)SA and OD–DADP algorithms were compared with the real-coded genetic algorithm (RGA) and particle swarm optimization (PSO) to analyze the performance of the four algorithms. The developed algorithms were applied to the Gao'a irrigation area in the north of Jiangsu Province, China. The solution results showed that the annual output value of water-deficient irrigation areas was improved, and limited water and land resources were optimally allocated, demonstrating the feasibility of the two-hybrid algorithm. Moreover, through a comparative analysis of the optimality and applicability of the four algorithms, it can be observed that (DADP–LP)SA and OD–DADP are more suitable for optimizing the allocation of scarce water and land resources than RGA and PSO.
{"title":"Application of hybrid algorithms in an optimal allocation model of water and land resources","authors":"Cong Wei, Jilin Cheng, Yushan Jiang","doi":"10.2166/hydro.2023.060","DOIUrl":"https://doi.org/10.2166/hydro.2023.060","url":null,"abstract":"\u0000 \u0000 Ensuring an optimal irrigation system and planting layout for crops in areas with water resource deficiencies is a complex process. A model of the optimal allocation of water and land resources for the irrigation system of the ‘reservoir and pumping station’ under crop rotation was established in this study. For the above complex nonlinear model, two-hybrid algorithms are proposed: (1) the decomposition aggregation dynamic programming (DADP) method and linear programming (LP) successive approximation algorithm [(DADP–LP)SA] and (2) the DADP algorithm based on the orthogonal design (OD) method (OD–DADP). The (DADP–LP)SA and OD–DADP algorithms were compared with the real-coded genetic algorithm (RGA) and particle swarm optimization (PSO) to analyze the performance of the four algorithms. The developed algorithms were applied to the Gao'a irrigation area in the north of Jiangsu Province, China. The solution results showed that the annual output value of water-deficient irrigation areas was improved, and limited water and land resources were optimally allocated, demonstrating the feasibility of the two-hybrid algorithm. Moreover, through a comparative analysis of the optimality and applicability of the four algorithms, it can be observed that (DADP–LP)SA and OD–DADP are more suitable for optimizing the allocation of scarce water and land resources than RGA and PSO.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44415428","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}
Freshwater availability is a very determining issue, especially in semiarid and arid regions, for sustainable development and secured food production. In this premise, the detection and assessment of water stress are of utmost importance. In this study, the satellite-based Potential Available Water Storage (PAWS) index is used to test its feasibility for a basin-scale analysis of water stress in the Western Mediterranean Basin (WMB) in Türkiye. The coarse-resolution GRACE (Gravity Recovery And Climate Experiment) estimates were downscaled based on the Random Forest (RF) model and then were integrated with fine-resolution precipitation data to derive fine-resolution PAWS values. The accuracy of the index was validated against the net water flux (NWF) and water storage deficit (WSD) values over the basin. The results revealed a good performance for the PAWS index for a local scale evaluation of water stress. The PAWS variations turned out to be highly correlated with the NWF (r = 0.72) and WSD (r = 0.66). The PAWS indicates that the WMB has suffered from a critical hydrological situation from 2003 to 2020 during which the basin has been under stress with the most critical situation in 2018 when the per capita water has fallen below 500 m3 suggesting an absolute water stress status.
{"title":"Satellite-based investigation of water stress at the basin scale: an integrated analysis of downscaled GRACE estimates and remotely sensed data","authors":"Behnam Khorrami","doi":"10.2166/hydro.2023.062","DOIUrl":"https://doi.org/10.2166/hydro.2023.062","url":null,"abstract":"\u0000 \u0000 Freshwater availability is a very determining issue, especially in semiarid and arid regions, for sustainable development and secured food production. In this premise, the detection and assessment of water stress are of utmost importance. In this study, the satellite-based Potential Available Water Storage (PAWS) index is used to test its feasibility for a basin-scale analysis of water stress in the Western Mediterranean Basin (WMB) in Türkiye. The coarse-resolution GRACE (Gravity Recovery And Climate Experiment) estimates were downscaled based on the Random Forest (RF) model and then were integrated with fine-resolution precipitation data to derive fine-resolution PAWS values. The accuracy of the index was validated against the net water flux (NWF) and water storage deficit (WSD) values over the basin. The results revealed a good performance for the PAWS index for a local scale evaluation of water stress. The PAWS variations turned out to be highly correlated with the NWF (r = 0.72) and WSD (r = 0.66). The PAWS indicates that the WMB has suffered from a critical hydrological situation from 2003 to 2020 during which the basin has been under stress with the most critical situation in 2018 when the per capita water has fallen below 500 m3 suggesting an absolute water stress status.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43590997","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}
This study aims at evaluating the performance of the Elman Neural Network (ENN), Elman Neural Network-Genetic Algorithm (ENN–GA), and Support Vector Machine-simulated annealing (SVM–SA) in determining the discharge of a newly proposed rotary gate for the inclusive data range from free flow to highly submerged conditions. For individual free and submerged flows, the models performed as well as that of the traditional relationships. However, the superiority of the intelligent models comes when dealing with the inclusive data set of both flow conditions, where no deterministic approach is available for discharge evaluation prior to specifying the threshold condition. In such complex flow conditions, the ENN–GA hybrid model with a proper structure determined the discharge with rather a high accuracy, i.e., SE of 6.12%. Also, in defining the threshold state, the ENN and ENN–GA models achieved superior results compared to the currently available relationship, i.e., it accurately recognized the threshold condition in almost 100% of the cases while the traditional relationship results were limited to 93% of the cases. Such accuracy of the employed model in assessing the discharge of the structure and its high ability in recognizing the flow state could be of great advantage for irrigation network structure automation.
{"title":"Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA","authors":"A. Marashi, S. Kouchakzadeh, H. Yonesi","doi":"10.2166/hydro.2023.202","DOIUrl":"https://doi.org/10.2166/hydro.2023.202","url":null,"abstract":"\u0000 \u0000 This study aims at evaluating the performance of the Elman Neural Network (ENN), Elman Neural Network-Genetic Algorithm (ENN–GA), and Support Vector Machine-simulated annealing (SVM–SA) in determining the discharge of a newly proposed rotary gate for the inclusive data range from free flow to highly submerged conditions. For individual free and submerged flows, the models performed as well as that of the traditional relationships. However, the superiority of the intelligent models comes when dealing with the inclusive data set of both flow conditions, where no deterministic approach is available for discharge evaluation prior to specifying the threshold condition. In such complex flow conditions, the ENN–GA hybrid model with a proper structure determined the discharge with rather a high accuracy, i.e., SE of 6.12%. Also, in defining the threshold state, the ENN and ENN–GA models achieved superior results compared to the currently available relationship, i.e., it accurately recognized the threshold condition in almost 100% of the cases while the traditional relationship results were limited to 93% of the cases. Such accuracy of the employed model in assessing the discharge of the structure and its high ability in recognizing the flow state could be of great advantage for irrigation network structure automation.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43870352","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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