Papers by Roghy Ghassempour
Estimates of monthly rainfall are important for various purposes such as flood estimation, drough... more Estimates of monthly rainfall are important for various purposes such as flood estimation, drought, irrigation planning, and river basin management. In the present study, the monthly rainfall of Tabriz station was investigated using the intelligent Gaussian Process Regression (GPR) method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Wavelet Transform (WT). Different models were defined based on teleconnection patterns and climatic elements, and the impact of different input parameters was assessed. The obtained results proved high capability and efficiency of the applied method in predicting the monthly precipitation. The results showed that time series decomposition based on wavelet transformation led to more accurate outcomes compared to the complementary ensemble empirical mode decomposition. The best evaluation of test series using wavelet transform decomposition was obtained for the state of modeling based on teleconnection patterns and climatic elem...
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Water Resources Management, 2021
Assessment of spatiotemporal variations of drought is an efficient method for implementing drough... more Assessment of spatiotemporal variations of drought is an efficient method for implementing drought mitigation strategies and reducing its negative impacts. This study aimed to assess the spatiotemporal pattern of short- to long-term droughts for an area with different climates. Therefore, 31 stations located in Iran were selected and the Standardized Precipitation Index (SPI) series with timescales of 3, 6, and 12 months were computed during the 1951-2016 period. A hybrid methodology including Maximal Overlap Discrete Wavelet Transform (MODWT) and K-means methods was used for obtaining the SPIs time-frequency properties and multiscale zoning of the area. The energy amounts of the decomposed subseries via the MODWT were applied as inputs for K-means. Also, the statistics in drought features (i.e. drought duration, severity, and peak) were assessed and the results showed that shorter term droughts (i.e. SPI-3 and -6) were more frequent and severe in the northern parts where the lowest values were obtained for drought duration. It was observed that the regions with more droughts frequency had the highest energy values. For shorter term droughts a direct relationship was obtained between the energy values and the mean SPIs, drought severity, and drought peak, whereas an inverse relationship was obtained for longer term drought. It was found that increasing the degree of SPI led to more similarity between the stations of each cluster. Also, the homogeneity of stations for the SPI-12 was slightly higher than the SPI-3 and -6.
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Journal of Hydroinformatics, 2021
Sediment transport is one of the most important issues in river engineering. In this study, the c... more Sediment transport is one of the most important issues in river engineering. In this study, the capability of the Kernel Extreme Learning Machine (KELM) approach for predicting the river daily Suspended Sediment Concentration (SSC) and Discharge (SSD) was assessed. Three successive hydrometric stations of Mississippi river were considered and based on the sediment and flow characteristics during the period of 2005–2008. Several models were developed and tested for SSC and SSD modeling. For improving the applied model efficiency, two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of modeling based on stations' own data (state 1) and previous stations' data (state 2) were considered. The single and integrated KELM model results comparison indicated that the integrated WT and EEMD-KELM models resulted in more accurate outcomes. Results showed that data processing with WT was more effective ...
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Hydrology Research
Beside in situ observations, satellite-based products can provide an ideal data source for spatio... more Beside in situ observations, satellite-based products can provide an ideal data source for spatiotemporal monitoring of drought. In this study, the spatiotemporal pattern of drought was investigated for the northwest part of Iran using ground- and satellite-based datasets. First, the Standardized Precipitation Index series were calculated via precipitation data of 29 sites located in the selected area and the CPC Merged Analysis of Precipitation satellite. The Maximal Overlap Discrete Wavelet Transform (MODWT) was used for obtaining the temporal features of time series, and further decomposition was performed using Ensemble Empirical Mode Decomposition (EEMD) to have more stationary time series. Then, multiscale zoning was done based on subseries energy values via two clustering methods, namely the self-organizing map and K-means. The results showed that the MODWT–EEMD–K-means method successfully identified homogenous drought areas. On the other hand, correlation between the satelli...
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Water Supply
The river stage–discharge relationship has an important impact on modeling, planning, and managem... more The river stage–discharge relationship has an important impact on modeling, planning, and management of river basins and water resources. In this study, the capability of the Gaussian Process Regressions (GPR) kernel-based approach was assessed in predicting the daily river stage–discharge (RSD) relationship. Three successive hydrometric stations of the Housatonic River were considered, and based on the flow characteristics during the period of 2002–2006 several models were developed and tested via GPR. To enhance the applied model efficiency, two pre-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of the RSD modeling were investigated. In state 1, each station's own data was used and in state 2, the upstream stations’ datasets were used as input to model the RSD downstream of the river. The single and integrated model results showed that the integrated WT- and EEMD-GPR models resulted in more acc...
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Hydrology Research
Drought as a severe natural disaster has devastating effects on the environment; therefore, relia... more Drought as a severe natural disaster has devastating effects on the environment; therefore, reliable drought prediction is an important issue. In the current study, based on lower upper bound estimation, hybrid models including data preprocessing, permutation entropy, and artificial intelligence (AI) methods were used for point and interval predictions of short- to long-term series of Standardized Precipitation Index in the Northwest of Iran. Ground-based and remote sensing precipitation data were used covering the period of 1983–2017. In the modeling process, first, the data processing capability via variational mode decomposition (VMD), ensemble empirical mode decomposition, and permutation entropy (PE) was investigated in drought point prediction. Then, interval prediction was applied for tolerating increased uncertainty and providing more details for practical operation decisions. The simulation results demonstrated that the proposed integrated models could achieve significantly...
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Journal of Hydroinformatics
Due to the drought negative impacts, accurate forecasting of drought indices is important. This s... more Due to the drought negative impacts, accurate forecasting of drought indices is important. This study focused on the short- to long-term Standardized Precipitation Index (SPI) forecasting in sites with different climates using newly integrated hybrid pre-post-processing techniques. Four sites in Iran's northwest were selected and the SPIs series with time scales of 3, 9, and 24 months were forecasted during the period of 1978–2017. For improving the modeling efficiency, wavelet transform and ensemble empirical mode decomposition (EEMD) pre-processing methods were used. In this regard, temporal features of the SPIs series were decomposed via wavelet transform (WT), then, the obtained sub-series were further broken down into intrinsic mode functions using EEMD. Also, simple linear averaging and nonlinear neural ensemble post-processing methods were applied to ensemble the outputs of hybrid models. The results showed that data pre-processing enhanced the models' capability up t...
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Water Supply
The hydraulic jump phenomenon is a beneficial tool in open channels for dissipating the extra ene... more The hydraulic jump phenomenon is a beneficial tool in open channels for dissipating the extra energy of the flow. The sequent depth ratio and hydraulic jump length critically contribute to designing hydraulic structures. In this research, the capability of the Support Vector Machine (SVM) and Gaussian Process Regression (GPR) as kernel-based approaches was evaluated to estimate the features of submerged and free hydraulic jumps in channels with rough elements and various shapes, followed by comparing the findings of the GPR and SVM models and the semi-empirical equations. The results represented the effect of the geometry (i.e., steps and roughness elements) of the applied appurtenances on hydraulic jump features in channels with appurtenances. Moreover, the findings confirmed the significance of the upstream Froude number in the estimating of sequent depth ratio in submerged and free hydraulic jumps. In addition, the immersion was the highest contributing variable regarding the sub...
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Proceedings
For transition of a supercritical flow into a subcritical flow in an open channel, a hydraulic ju... more For transition of a supercritical flow into a subcritical flow in an open channel, a hydraulic jump phenomenon is used. Different shaped channels are used as useful tools in the extra energy dissipation of the hydraulic jump. Accurate prediction of relative energy dissipation is important in designing hydraulic structures. The aim of this paper is to assess the capability of a Kernel extreme Learning Machine (KELM) meta-model approach in predicting the energy dissipation in different shaped channels (i.e., rectangular and trapezoidal channels). Different experimental data series were used to develop the models. The obtained results approved the capability of the KELM model in predicting the energy dissipation. Results showed that the rectangular channel led to better outcomes. Based on the results obtained for the rectangular and trapezoidal channels, the combination of Fr1, (y2-y1)/y1, and W/Z parameters performed more successfully. Also, comparison between KELM and the Artificial ...
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Water Supply
Sediment transportation and accurate estimation of its rate is a significant issue for river engi... more Sediment transportation and accurate estimation of its rate is a significant issue for river engineers and researchers. In this study, the capability of kernel based approaches including Kernel Extreme Learning Machine (KELM) and Gaussian Process Regression (GPR) was assessed for predicting the river daily Suspended Sediment Discharge (SSD). For this aim, the Mississippi river, with three consecutive hydrometric stations, was selected as the case study. Based on the sediment and flow characteristics during the period of 2005–2008, several models were developed and tested under two scenarios (i.e. modeling based on each station's own data or the previous stations' data). Two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used for enhancing the SSD modeling capability. Also, data post-proceeding was done using Simple Linear Averaging (SLAM) and Nonlinear Kernel Extreme Learning Machine Ensemble (NKELME) methods....
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Journal of Hydroinformatics
An accurate prediction of roughness coefficient in alluvial channels is of substantial importance... more An accurate prediction of roughness coefficient in alluvial channels is of substantial importance for river management. In this study, the total and form resistance in alluvial channels with dune bedform were assessed using experimental data. First, the data of experiments carried out at the Hydraulic Laboratory of University of Tabriz was used to investigate the impact of hydraulic and sediment parameters on roughness coefficient. Then, these data were combined with other laboratory data, and the total and bedform resistance were modeled via a Gaussian Process Regression (GPR) approach. For models, developing different input combinations were considered based on flow and sediment characteristics. The obtained results from the experiments showed that the Reynolds number has a better correlation with flow resistance in comparison with other hydraulic parameters. It was found that the roughness variations due to bedform are almost between 40 and 80% of the total roughness coefficient....
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Journal of Hydroinformatics
Energy dissipation in culverts is a complex phenomenon due to the nonlinearity and uncertainties ... more Energy dissipation in culverts is a complex phenomenon due to the nonlinearity and uncertainties of the process. In the current study, the capability of Gaussian process regression (GPR) and support vector machine (SVM) as kernel-based approaches and the GEP method was assessed in predicting energy losses in culverts. Two types of bend loss in rectangular culverts and entrance loss in circular culverts with different inlet end treatments were considered. Various input combinations were developed and tested using experimental data. The OAT (one-at-a-time), factorial sensitivity analysis and Monte-Carlo uncertainty analysis were used to select the effective parameters in modeling. The results of performance criteria proved capability of the applied methods (i.e. high R and DC and low RMSE). For rectangular culverts, the model with parameters Fr (Froude number) and θ (bend angle), and for circular culverts, the model with parameters Fr and Hw/D (depth ratio), were the superior models. ...
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International Journal of Sediment Research
Abstract One of the important issues in water transport and sewer systems is determining the flow... more Abstract One of the important issues in water transport and sewer systems is determining the flow resistance and roughness coefficient. An accurate estimation of the roughness coefficient is a substantial issue in the design and operation of hydraulic structures such as sewer pipes, the calculation of water depth and flow velocity, and the accurate characterization of energy losses. The current study, applies two kernel based approaches [Support Vector Machine (SVM) and Gaussian Process Regression (GPR)] to develop roughness coefficient models for sewer pipes. In the modeling process, two types of sewer bed conditions were considered: loose bed and rigid bed. In order to develop the models, different input combinations were considered under three scenarios (Scenario 1: based on hydraulic characteristics, Scenarios 2 and 3: based on hydraulic and sediment characteristics with and without considering sediment concentration as input). The results proved the capability of the kernel based approaches in prediction of the roughness coefficient and it was found that for prediction of this parameter in sewer pipes Scenario 3 performed better than Scenarios 1 and 2. Also, the sensitivity analysis results showed that Dgr (Dimensionless particle number) for a rigid bed and wb/y (ratio of deposited bed width, wb, to flow depth, y) for a loose bed had the most significant impact on the modeling process.
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Water Supply
One of the hydraulic phenomena that mainly occurs during the water withdrawal process of channels... more One of the hydraulic phenomena that mainly occurs during the water withdrawal process of channels is the formation of vortices that can cause many problems for the hydro-mechanical facilities of intakes. In the current study, classical models and meta model approaches (i.e. Support Vector Machine and Gene Expression Programming) were applied to evaluate the impact of pipe diameter and hydraulic condition changes in prediction of the critical submergence depth ratio in horizontal intakes. In this regard, two types of critical submergence experiments, based on bottom clearance, were considered (i.e. c = 0 and c = d/2, in which c and d are the bottom clearance and diameter of the intake, respectively). Different models were developed and tested using experimental data series. The results indicated that in modeling the critical submergence depth ratio, meta model approaches led to better predictions compared to the classical approaches. It was observed that the developed models for the ...
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Water Science and Technology: Water Supply
Sedimentation in sewer pipes has a negative impact on the performance of sewerage systems. Howeve... more Sedimentation in sewer pipes has a negative impact on the performance of sewerage systems. However, due to the complex nature of sedimentation, determining the governing equations is difficult and the results of the available classic models for computing bedload transport rate often differ from each other. This paper focuses on the capability of a support vector machine (SVM) as a meta-model approach for predicting bedload transport in pipes. The method was applied for the deposition and limit of deposition states of sediment transport. Two different scenarios were proposed: in Scenario 1, the input combinations were prepared using only hydraulic characteristics, on the other hand, Scenario 2 was built using both hydraulic and sediment characteristics as model inputs of bedload transport. A comparison between the SVM and the employed classic approaches in predicting sediment transport indicated the supreme performance of the SVM, in which more accurate results were obtained. Also it...
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Journal of Hydroinformatics
Rough bed channels are one of the appurtenances used to dissipate the extra energy of the flow th... more Rough bed channels are one of the appurtenances used to dissipate the extra energy of the flow through hydraulic jump. The aim of this paper is to assess the effects of channel geometry and rough boundary conditions (i.e., rectangular, trapezoidal, and expanding channels with different rough elements) in predicting the hydraulic jump energy dissipation using support vector machine (SVM) as a meta-model approach. Using different experimental data series, different models were developed with and without considering dimensional analysis. The results approved capability of the SVM model in predicting the relative energy dissipation. It was found that the developed models for expanding channel with central sill performed more successfully and, for this case, superior performance was obtained for the model with parameters Fr1 and h1/B. Considering the rectangular and trapezoidal channels, the model with parameters Fr1, (h2−h1)/h1, W/Z led to better predictions. It was observed that betwee...
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International Journal of Sediment Research
Abstract Sediment transport is a complex phenomenon due to the nonlinearity and uncertainties of ... more Abstract Sediment transport is a complex phenomenon due to the nonlinearity and uncertainties of the process. The present study applies Gene Expression Programming (GEP) to develop bedload transport models in sewer pipes. In this regard, two types of bedload were considered: loose bed (deposition state) and rigid bed (limit of deposition state). In order to develop the models, two scenarios with different input combinations were considered: Scenario 1 considers only hydraulic characteristics and Scenario 2 considers both hydraulic and sediment characteristics as inputs for modeling bedload discharge. The results proved the capability of GEP in prediction of sediment transport and it was found that for prediction of bedload transport in sewer pipes Scenario 2 performed more successfully than Scenario 1. According to the outcome of sensitivity analysis, Frm (Modified Froude number) and d50/y (relative particle size) for rigid boundary and Frm for loose boundary had key roles in the modeling. The outcome of the GEP models also was compared with existing empirical equations and it was found the GEP models yielded better results. It was also found that pipe diameter affected the transport capacity of the sewer pipe. Increasing pipe diameter caused an increase in model efficiency. A pipe with a diameter of 305 mm yielded to the best results.
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Water Science and Technology
Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinet... more Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic structures. This paper focuses on the capability of the support vector machine (SVM) as a meta-model approach for predicting hydraulic jump characteristics in different sudden diverging stilling basins (i.e. basins with and without appurtenances). In this regard, different models were developed and tested using 1,018 experimental data. The obtained results proved the capability of the SVM technique in predicting hydraulic jump characteristics and it was found that the developed models for a channel with a central block performed more successfully than models for channels without appurtenances or with a negative step. The superior performance for the length of hydraulic jump was obtained for the model with parameters F1 (Froude number) and (h...
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Hydrology Research
Hydraulic jump is a useful means of dissipating excess energy of a supercritical flow so that obj... more Hydraulic jump is a useful means of dissipating excess energy of a supercritical flow so that objectionable scour downstream is minimized. The present study applies gene expression programming (GEP) to estimate hydraulic jump characteristics in sudden expanding channels. Three types of expanding channels were considered: channels without appurtenances, with a central sill, and with a negative step. 1,000 experimental data were considered as input data to develop models. The results proved the capability of GEP in predicting hydraulic jump characteristics in expanding channels. It was found that the developed models for channel with a central sill performed better than other channels. In the jump length prediction, the model with input parameters Fr1 and (y2—y1)/y1, and in the sequent depth ratio and relative energy dissipation prediction the model with input parameters Fr1 and y1/B led to more accurate outcomes (Fr1, y1, y2, and B are Froude number, sequent depth of upstream and dow...
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Papers by Roghy Ghassempour