Pub Date : 2023-07-06DOI: 10.1080/02626667.2023.2221790
E. Jang, U. Ji, W. Yeo
ABSTRACT The model tree (MT) approach, a data mining technique used to analyse relationships between input and output variables in a disordered and large database, was adopted in this study to predict sediment discharge with field measurement data. The derived models were analysed for accuracy according to the goodness of fit based on training, testing, and modelling processes. When the flow velocity, depth, water surface slope, channel width, and median bed material were selected as the river’s system variables, the model results of sediment discharge resembled the measured values. The results demonstrate that developing and using the sediment discharge estimation with the MT constitutes the most effective method if long-term sediment data are of sufficient validity.
{"title":"Estimation of sediment discharge using a tree-based model","authors":"E. Jang, U. Ji, W. Yeo","doi":"10.1080/02626667.2023.2221790","DOIUrl":"https://doi.org/10.1080/02626667.2023.2221790","url":null,"abstract":"ABSTRACT The model tree (MT) approach, a data mining technique used to analyse relationships between input and output variables in a disordered and large database, was adopted in this study to predict sediment discharge with field measurement data. The derived models were analysed for accuracy according to the goodness of fit based on training, testing, and modelling processes. When the flow velocity, depth, water surface slope, channel width, and median bed material were selected as the river’s system variables, the model results of sediment discharge resembled the measured values. The results demonstrate that developing and using the sediment discharge estimation with the MT constitutes the most effective method if long-term sediment data are of sufficient validity.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49446033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-05DOI: 10.1080/02626667.2023.2224002
I. Gnecco, A. Palla, P. La Barbera, G. Roth, F. Giannoni
ABSTRACT In the field of sub-hourly durations, and especially in urban hydrology, selecting the most appropriate form of the intensity–duration–frequency (IDF) curve becomes a relevant question. In this study, two different formulations of IDF curves – that are characterized by a curvature in the sub-hourly intervals and a power-law formulation for the super-hourly intervals – are proposed in order to maximize the overall information contribution in the sub-hourly and the super-hourly domains. The proposed formulations are compared with two well-known IDF formulations, respectively characterized by a power-law and a curvature (from the power-law) formulation, calibrated only using data referring to super-hourly durations. Findings indicated that the proposed IDF curves allow to account for the different lengths of the sub-hourly and canonical data series and eventually for the different behaviour/trend of sub-hourly and super-hourly data, thus providing the best reliability indicator, at least in the investigated return period.
{"title":"Defining intensity–duration–frequency curves at short durations: a methodological framework","authors":"I. Gnecco, A. Palla, P. La Barbera, G. Roth, F. Giannoni","doi":"10.1080/02626667.2023.2224002","DOIUrl":"https://doi.org/10.1080/02626667.2023.2224002","url":null,"abstract":"ABSTRACT In the field of sub-hourly durations, and especially in urban hydrology, selecting the most appropriate form of the intensity–duration–frequency (IDF) curve becomes a relevant question. In this study, two different formulations of IDF curves – that are characterized by a curvature in the sub-hourly intervals and a power-law formulation for the super-hourly intervals – are proposed in order to maximize the overall information contribution in the sub-hourly and the super-hourly domains. The proposed formulations are compared with two well-known IDF formulations, respectively characterized by a power-law and a curvature (from the power-law) formulation, calibrated only using data referring to super-hourly durations. Findings indicated that the proposed IDF curves allow to account for the different lengths of the sub-hourly and canonical data series and eventually for the different behaviour/trend of sub-hourly and super-hourly data, thus providing the best reliability indicator, at least in the investigated return period.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43597202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-04DOI: 10.1080/02626667.2023.2232356
F. Vilaseca, A. Castro, C. Chreties, A. Gorgoglione
ABSTRACT This work aims to improve the feature selection for data-driven rainfall–runoff models by assessing the significance of each input variable in the learning process and analysing it from a physical point of view. For this purpose, a set of 14 experiments was carried out in two watersheds of the Santa Lucía Chico basin, Uruguay. A random forest model was trained and tested for daily discharge prediction in each of them using different input variables. A feature importance analysis was carried out for each model, using a non-model-biased method (Shapely additive explanations). Results showed that the most relevant variables were lagged discharges of one and two days, along with seven-day accumulated rainfall, which is interpreted as a proxy of the soil moisture condition of the watershed. The temperature was also relevant and was proven to represent the effect of the whole set of climatic variables (relative humidity, solar radiation, wind speed).
{"title":"Assessing influential rainfall–runoff variables to simulate daily streamflow using random forest","authors":"F. Vilaseca, A. Castro, C. Chreties, A. Gorgoglione","doi":"10.1080/02626667.2023.2232356","DOIUrl":"https://doi.org/10.1080/02626667.2023.2232356","url":null,"abstract":"ABSTRACT This work aims to improve the feature selection for data-driven rainfall–runoff models by assessing the significance of each input variable in the learning process and analysing it from a physical point of view. For this purpose, a set of 14 experiments was carried out in two watersheds of the Santa Lucía Chico basin, Uruguay. A random forest model was trained and tested for daily discharge prediction in each of them using different input variables. A feature importance analysis was carried out for each model, using a non-model-biased method (Shapely additive explanations). Results showed that the most relevant variables were lagged discharges of one and two days, along with seven-day accumulated rainfall, which is interpreted as a proxy of the soil moisture condition of the watershed. The temperature was also relevant and was proven to represent the effect of the whole set of climatic variables (relative humidity, solar radiation, wind speed).","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48040564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-04DOI: 10.1080/02626667.2023.2232348
Y. Tan, J. L. Ng, Yuk Feng Huang
ABSTRACT Drought assessment has attracted attention in the research community, especially regarding the accuracy of drought indices due to input data uncertainty. This study addressed the impacts of input data uncertainty on the estimation of the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) for drought assessment in Peninsular Malaysia. The Kolmogorov-Smirnov test recommended the gamma distribution function for the SPI and the log-logistic distribution function for the SPEI. The bootstrap method was used to estimate SPIU and SPEIU, which account for input data uncertainty, and provided estimates for SPI and SPEI values. However, the standard deviation indicated significant input data uncertainty, with values ranging from 0.1038 to 0.1378. The two drought indices exhibited similar classifications of drought categories, but SPIU showed greater uncertainty for very dry and extremely dry events. The findings emphasize the importance of input data uncertainty, especially when dealing with extreme drought events.
{"title":"Quantitative analysis of input data uncertainty for SPI and SPEI in Peninsular Malaysia based on the bootstrap method","authors":"Y. Tan, J. L. Ng, Yuk Feng Huang","doi":"10.1080/02626667.2023.2232348","DOIUrl":"https://doi.org/10.1080/02626667.2023.2232348","url":null,"abstract":"ABSTRACT Drought assessment has attracted attention in the research community, especially regarding the accuracy of drought indices due to input data uncertainty. This study addressed the impacts of input data uncertainty on the estimation of the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) for drought assessment in Peninsular Malaysia. The Kolmogorov-Smirnov test recommended the gamma distribution function for the SPI and the log-logistic distribution function for the SPEI. The bootstrap method was used to estimate SPIU and SPEIU, which account for input data uncertainty, and provided estimates for SPI and SPEI values. However, the standard deviation indicated significant input data uncertainty, with values ranging from 0.1038 to 0.1378. The two drought indices exhibited similar classifications of drought categories, but SPIU showed greater uncertainty for very dry and extremely dry events. The findings emphasize the importance of input data uncertainty, especially when dealing with extreme drought events.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42661187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1080/02626667.2023.2231434
Eduardo Muñoz-Castro, P. Mendoza, N. Vásquez, X. Vargas
ABSTRACT We examine the extent to which the parameters of different types of catchments are sensitive to calibration criteria selection (i.e. parameter agreement), and explore possible connections with overall model performance and model complexity. To this end, we calibrate the lumped GR4J, GR5J and GR6J hydrological models – coupled with the CemaNeige snow module – in 95 catchments spanning a myriad of hydroclimatic and physiographic characteristics across Chile, using 12 streamflow-oriented objective functions. The results show that (i) the choice of objective function has smaller effects on parameter values in catchments with low aridity index and high mean annual runoff ratio, in contrast to drier climates; and (ii) catchments with better parameter agreement also provide better performance across model structures and simulation periods. More generally, this work provides insights on the type of catchments where it is more challenging to find sub-domains in the parameter space that satisfy multiple streamflow criteria.
{"title":"Exploring parameter (dis)agreement due to calibration metric selection in conceptual rainfall–runoff models","authors":"Eduardo Muñoz-Castro, P. Mendoza, N. Vásquez, X. Vargas","doi":"10.1080/02626667.2023.2231434","DOIUrl":"https://doi.org/10.1080/02626667.2023.2231434","url":null,"abstract":"ABSTRACT We examine the extent to which the parameters of different types of catchments are sensitive to calibration criteria selection (i.e. parameter agreement), and explore possible connections with overall model performance and model complexity. To this end, we calibrate the lumped GR4J, GR5J and GR6J hydrological models – coupled with the CemaNeige snow module – in 95 catchments spanning a myriad of hydroclimatic and physiographic characteristics across Chile, using 12 streamflow-oriented objective functions. The results show that (i) the choice of objective function has smaller effects on parameter values in catchments with low aridity index and high mean annual runoff ratio, in contrast to drier climates; and (ii) catchments with better parameter agreement also provide better performance across model structures and simulation periods. More generally, this work provides insights on the type of catchments where it is more challenging to find sub-domains in the parameter space that satisfy multiple streamflow criteria.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46344270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-29DOI: 10.1080/02626667.2023.2231437
J. Romero-Cuellar, F. Francés
ABSTRACT Climate change impact models (CCIMs) suffer from inherent bias, uncertainty, and asynchronous observations in the baseline period. To overcome these challenges, this study introduces a methodology to assess CCIMs in the baseline period using the uncertainty analysis of streamflow statistics via the approximate Bayesian computation (ABC) post-processor, which infers the residual error model parameters based on summary statistics (signatures). As an illustrative case study, we analyzed the climate change projections of the fifth assessment report of the United Nations intergovernmental panel on climate change (AR5 - IPCC) of the monthly streamflow in the upper Oria catchment (Spain) with deterministic and probabilistic verification frameworks to assess the ABC post-processor outputs. In addition, the ABC post-processor is evaluated against the ensemble (reference method). The results show that the ABC post-processor outperformed the ensemble method in all verification metrics, and the ensemble method has reasonable reliability but exhibited poor sharpness. We suggest that the ensemble method should be complemented with the ABC post-processor for climate change impact studies.
{"title":"How to assess climate change impact models: uncertainty analysis of streamflow statistics via approximate Bayesian computation (ABC)","authors":"J. Romero-Cuellar, F. Francés","doi":"10.1080/02626667.2023.2231437","DOIUrl":"https://doi.org/10.1080/02626667.2023.2231437","url":null,"abstract":"ABSTRACT Climate change impact models (CCIMs) suffer from inherent bias, uncertainty, and asynchronous observations in the baseline period. To overcome these challenges, this study introduces a methodology to assess CCIMs in the baseline period using the uncertainty analysis of streamflow statistics via the approximate Bayesian computation (ABC) post-processor, which infers the residual error model parameters based on summary statistics (signatures). As an illustrative case study, we analyzed the climate change projections of the fifth assessment report of the United Nations intergovernmental panel on climate change (AR5 - IPCC) of the monthly streamflow in the upper Oria catchment (Spain) with deterministic and probabilistic verification frameworks to assess the ABC post-processor outputs. In addition, the ABC post-processor is evaluated against the ensemble (reference method). The results show that the ABC post-processor outperformed the ensemble method in all verification metrics, and the ensemble method has reasonable reliability but exhibited poor sharpness. We suggest that the ensemble method should be complemented with the ABC post-processor for climate change impact studies.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48302753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-28DOI: 10.1080/02626667.2023.2224005
I. Matiatos, A. Papadopoulos, Y. Panagopoulos, E. Dimitriou
ABSTRACT Water isotopes (δ18O, δ2H) were systematically monitored in two river catchments to investigate the isotopic spatiotemporal variation and the differences between them. The Pinios River Basin (PRB) exhibited lower average δ 18O and δ 2H values (−7.9‰ and −50.8‰, respectively) compared to the Evrotas River Basin (ERB) (−6.5‰ and −38.2‰, respectively) but higher in range (3.3‰ vs 1.2‰ for δ18O, respectively). The Bayesian modelling results showed higher groundwater contribution in the PRB (25–50%) than in the ERB (15–35%) relative to precipitation during the wet period. The isotopic spatial variability was attributed to the influence of local precipitation, evaporation and additional flow pathways (e.g. soil water). The correlation analysis showed that the isotopic composition is controlled by the catchment altitude, slope and discharge. This study highlights the catchment physiographic control on the isotopic composition of rivers, which can support strategies for better water resources management.
{"title":"Insights into the influence of morphology on the hydrological processes of river catchments using stable isotopes","authors":"I. Matiatos, A. Papadopoulos, Y. Panagopoulos, E. Dimitriou","doi":"10.1080/02626667.2023.2224005","DOIUrl":"https://doi.org/10.1080/02626667.2023.2224005","url":null,"abstract":"ABSTRACT Water isotopes (δ18O, δ2H) were systematically monitored in two river catchments to investigate the isotopic spatiotemporal variation and the differences between them. The Pinios River Basin (PRB) exhibited lower average δ 18O and δ 2H values (−7.9‰ and −50.8‰, respectively) compared to the Evrotas River Basin (ERB) (−6.5‰ and −38.2‰, respectively) but higher in range (3.3‰ vs 1.2‰ for δ18O, respectively). The Bayesian modelling results showed higher groundwater contribution in the PRB (25–50%) than in the ERB (15–35%) relative to precipitation during the wet period. The isotopic spatial variability was attributed to the influence of local precipitation, evaporation and additional flow pathways (e.g. soil water). The correlation analysis showed that the isotopic composition is controlled by the catchment altitude, slope and discharge. This study highlights the catchment physiographic control on the isotopic composition of rivers, which can support strategies for better water resources management.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41639298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-28DOI: 10.1080/02626667.2023.2220885
Behzad Zohourian, Seyed Mahmood Hosseini
ABSTRACT This study explores the use of gene expression programming (GEP) and artificial neural networks (ANNs) to estimate flood index values based on the unit flood response (UFR) method in two adjacent watersheds (Ardak and Kardeh) located in northeast Iran. The performances of the studied data-driven models were compared according to certain statistical measures such as Root Mean Square Error (RMSE). The findings indicate that GEP models were more accurate than ANNs (RMSE = 0.0986 vs. 0.1512 for Ardak and RMSE = 0.1024 vs. 0.1112 for Kardeh, respectively). Another advantage of the GEP models was providing an explicit relationship between flood index values and physical attributes. As flood index values derived via the UFR method were close to each other, flood contribution area maps were developed using a geographic information system (GIS) to consider uncertainty. Then, fusion algorithms including ordinary averaging, linear regression, and GEP were applied to develop a flexible regional model.
摘要:本研究探讨了利用基因表达编程(GEP)和人工神经网络(ann)基于单位洪水响应(UFR)方法估算伊朗东北部两个相邻流域(Ardak和Kardeh)的洪水指数值。根据一定的统计指标，如均方根误差(RMSE)，比较了所研究的数据驱动模型的性能。结果表明，GEP模型比人工神经网络更准确(Ardak的RMSE = 0.0986 vs. 0.1512, Kardeh的RMSE = 0.1024 vs. 0.1112)。GEP模型的另一个优点是提供了洪水指数值与物理属性之间的明确关系。由于通过UFR方法得到的洪水指数彼此接近，因此使用地理信息系统(GIS)开发洪水贡献区图，以考虑不确定性。然后，应用普通平均、线性回归和GEP等融合算法建立柔性区域模型。
{"title":"Determining flood source areas in watersheds using data-driven models and a geographic information system","authors":"Behzad Zohourian, Seyed Mahmood Hosseini","doi":"10.1080/02626667.2023.2220885","DOIUrl":"https://doi.org/10.1080/02626667.2023.2220885","url":null,"abstract":"ABSTRACT This study explores the use of gene expression programming (GEP) and artificial neural networks (ANNs) to estimate flood index values based on the unit flood response (UFR) method in two adjacent watersheds (Ardak and Kardeh) located in northeast Iran. The performances of the studied data-driven models were compared according to certain statistical measures such as Root Mean Square Error (RMSE). The findings indicate that GEP models were more accurate than ANNs (RMSE = 0.0986 vs. 0.1512 for Ardak and RMSE = 0.1024 vs. 0.1112 for Kardeh, respectively). Another advantage of the GEP models was providing an explicit relationship between flood index values and physical attributes. As flood index values derived via the UFR method were close to each other, flood contribution area maps were developed using a geographic information system (GIS) to consider uncertainty. Then, fusion algorithms including ordinary averaging, linear regression, and GEP were applied to develop a flexible regional model.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48032602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-27DOI: 10.1080/02626667.2023.2230197
H. Hamidifar, M. Nones
ABSTRACT The impact of dams on fluvial hydro-morphology in arid and semi-arid regions, such as the Middle East, has received little attention, although such fluvial corridors represent major sources of livelihood for the local population. In this study, the influence of the Mollasadra Dam on the hydro-morphological conditions of the Iranian Kor River is numerically investigated using iRIC MFlow_02, by considering different flooding conditions. Simulating different scenarios with and without the dam, and looking at key parameters like flow rate and velocity, sediment transport rate, and bed topography, it is evident that the dam significantly reduces the water discharge and the relative flow velocities, while it has a relatively low impact on the bed morphology and sediment transport. This can be ascribed to the fact that the Kor River is a capacity-limited watercourse, characterized by rather coarse material and reduced sediment transport.
{"title":"Hydro-morphodynamic responses of rivers to the construction of hydropower dams: a case study – the Kor River, Iran","authors":"H. Hamidifar, M. Nones","doi":"10.1080/02626667.2023.2230197","DOIUrl":"https://doi.org/10.1080/02626667.2023.2230197","url":null,"abstract":"ABSTRACT The impact of dams on fluvial hydro-morphology in arid and semi-arid regions, such as the Middle East, has received little attention, although such fluvial corridors represent major sources of livelihood for the local population. In this study, the influence of the Mollasadra Dam on the hydro-morphological conditions of the Iranian Kor River is numerically investigated using iRIC MFlow_02, by considering different flooding conditions. Simulating different scenarios with and without the dam, and looking at key parameters like flow rate and velocity, sediment transport rate, and bed topography, it is evident that the dam significantly reduces the water discharge and the relative flow velocities, while it has a relatively low impact on the bed morphology and sediment transport. This can be ascribed to the fact that the Kor River is a capacity-limited watercourse, characterized by rather coarse material and reduced sediment transport.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46217114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-27DOI: 10.1080/02626667.2023.2230202
A. Sahin, Emin Çiftçi
ABSTRACT In this study we propose a new method called the cessation time approach (CTA) for interpreting recovery tests in confined aquifers, which is based on the Theis solution. The CTA method involves selecting a residual drawdown measurement from the recovery phase and linking it to its dimensionless counterpart through simple algebraic steps. This approach is then incorporated with a regression model to estimate aquifer parameters. The performance of several parametric polynomial and non-parametric machine learning regression models was investigated using various datasets. Results show that CTA with third-order multivariable polynomials produced highly accurate parameter estimates with a normalized root mean squared error (NRMSE) within 0.5% for a field dataset. Among the machine learning algorithms tested, the radial basis function and Gaussian process regression achieved the highest accuracy with NRMSEs of 0.6%. We conclude that CTA can be a viable interpretation tool for recovery tests due to its accuracy and simplicity.
{"title":"Cessation time approach incorporating parametric and non-parametric machine-learning algorithms for recovery test data","authors":"A. Sahin, Emin Çiftçi","doi":"10.1080/02626667.2023.2230202","DOIUrl":"https://doi.org/10.1080/02626667.2023.2230202","url":null,"abstract":"ABSTRACT In this study we propose a new method called the cessation time approach (CTA) for interpreting recovery tests in confined aquifers, which is based on the Theis solution. The CTA method involves selecting a residual drawdown measurement from the recovery phase and linking it to its dimensionless counterpart through simple algebraic steps. This approach is then incorporated with a regression model to estimate aquifer parameters. The performance of several parametric polynomial and non-parametric machine learning regression models was investigated using various datasets. Results show that CTA with third-order multivariable polynomials produced highly accurate parameter estimates with a normalized root mean squared error (NRMSE) within 0.5% for a field dataset. Among the machine learning algorithms tested, the radial basis function and Gaussian process regression achieved the highest accuracy with NRMSEs of 0.6%. We conclude that CTA can be a viable interpretation tool for recovery tests due to its accuracy and simplicity.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46687578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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