Hamed Zamani Sabzi (Zamanisabzi) | University of Oklahoma (original) (raw)
Papers by Hamed Zamani Sabzi (Zamanisabzi)
This study assessed the performances of the traditional temperature-index snowmelt runoff model (... more This study assessed the performances of the traditional temperature-index snowmelt runoff model (SRM) and an SRM model with a finer zonation based on aspect and slope (SRM þ AS model) in a data-scarce mountain watershed in the Urumqi River Basin, in Northwest China. The proposed SRM þ AS model was used to estimate the melt rate with the degree-day factor (DDF) through the division of watershed elevation zones based on aspect and slope. The simulation results of the SRM þ AS model were compared with those of the traditional SRM model to identify the improvements of the SRM þ AS model's performance with consideration of topographic features of the watershed. The results show that the performance of the SRM þ AS model has improved slightly compared to that of the SRM model. The coefficients of determination increased from 0.73, 0.69, and 0.79 with the SRM model to 0.76, 0.76, and 0.81 with the SRM þ AS model during the simulation and validation periods in 2005, 2006, and 2007, respectively. The proposed SRM þ AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization, careful input data selection, and data preparation.
Since fresh water is limited while agricultural and human water demands are continuously increasi... more Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models – autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) – were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predic-tors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.
Since fresh water is limited while agricultural and human water demands are continuously increasi... more Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models – autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) – were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predic-tors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.
Since fresh water is limited while agricultural and human water demands are continuously increasi... more Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models – autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) – were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predic-tors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.
This study assessed the performances of the traditional temperature-index snowmelt runoff model (... more This study assessed the performances of the traditional temperature-index snowmelt runoff model (SRM) and an SRM model with a finer zonation based on aspect and slope (SRM þ AS model) in a data-scarce mountain watershed in the Urumqi River Basin, in Northwest China. The proposed SRM þ AS model was used to estimate the melt rate with the degree-day factor (DDF) through the division of watershed elevation zones based on aspect and slope. The simulation results of the SRM þ AS model were compared with those of the traditional SRM model to identify the improvements of the SRM þ AS model's performance with consideration of topographic features of the watershed. The results show that the performance of the SRM þ AS model has improved slightly compared to that of the SRM model. The coefficients of determination increased from 0.73, 0.69, and 0.79 with the SRM model to 0.76, 0.76, and 0.81 with the SRM þ AS model during the simulation and validation periods in 2005, 2006, and 2007, respectively. The proposed SRM þ AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization, careful input data selection, and data preparation.
Decision-making processes in water resources projects are often multi-criteria, and numerous tech... more Decision-making processes in water resources projects are often multi-criteria, and numerous techniques have been developed for evaluating these projects. The main concern in utilizing multi-criteria decision-making (MCDM) techniques is that different techniques may result different outputs, therefore, selecting an appropriate technique is crucially important. Most decision makers prefer simple and transparent decision-making approaches which simultaneously show the trade-offs among the different decisions. This study utilizes multiple comparisons of MCDM techniques to interpret the similarities and dissimilarities of those methods and their consequences in the same project, which is multi-criteria management of stochastic floods in the Sunland Park area (Diez Lagos) in southern New Mexico. The objectives of the Diez Lagos flood control system are flood damage reduction (FDR), increasing usable water supply (WS) from stochastic floods, E. coli remediation (ER) from storm water, riparian habitat restoration (RHR), and human health and safety (HHS) in the study area. For all techniques, we simulated the same decision in the form of a decision matrix with m alternatives (flood control rules) against n criteria (FDR, WS, ER, RHR, HHS, and related Costs). We investigate six techniques: TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (in Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje), SAW (Simple Additive Weights), AHP (Analytic Hierarchy Process), ELECTRE (Elimination et Choice Translating Reality), and Compromise Programming (CP). The evaluation of the numerical results from this study can lead to the selection of the best decision-making technique, which can be extended to other projects.
Different multi-criteria decision-making (MCDM) techniques require different levels of computatio... more Different multi-criteria decision-making (MCDM) techniques require different levels of computational intensity and may produce different outputs, so selecting an appropriate technique largely determines the quality of the recommended decision and the effort required to obtain that decision. In most real environments , criteria and their constraints are not deterministic and cannot be specified precisely; therefore, those criteria are uncertain or fuzzy. To facilitate the selection of an appropriate MCDM method under a fuzzy environment, this study investigates and statistically compares the performances of ten commonly used MCDM techniques: simple additive weights (SAW), weighted product method (WPM), compromise programming (CP), technique for order preference by similarity to ideal solution (TOPSIS), four types of analytical hierarchy process (AHP), VIKOR (in Serbian: VIseKriterijumska Optimizacija I Kompromisno Re-senje), and ELECTRE (in French: ELimination Et Choix Traduisant la REalité). These techniques' performances were compared using fuzzy criteria and constraints, matching the conditions usually found in real applications. To conduct the comparisons, the 10 multi-criteria decision ranking methods were applied to 1250 simulated sets of decision matrices with fuzzy triangular values, and 12,500 sets of ranks were analyzed to compare the ranking methods. SAW and TOPSIS had statistically similar performances. ELECTRE was not preferable in providing full, sorted ranks among the alternatives. VIKOR considering its ranking process, for specific conditions, assigns identical ranks for several alternatives; when full, sorted ranks are required, VIKOR is unfavorable, although it is a powerful technique in introducing the closest alternative to the ideal condition. Types 1 and 3 of AHP and types 2 and 4 of AHP had close performances. Notably, no ranking method was significantly sensitive to uncertainty levels when uncertainty changed symmetrically.
In fuzzy logic controllers (FLCs), optimal performance can be defined as performance that minimiz... more In fuzzy logic controllers (FLCs), optimal performance can be defined as performance that minimizes the deviation (error term) between the decisions of the fuzzy logic systems and the decisions of experts. A range of approaches – such as genetic algorithms (GA), particle swarm optimization (PSO), artificial neural networks (ANN), and adaptive network based fuzzy inference systems (ANFIS) – can be used to pursue optimal performance for FLCs by refining the membership function parameters (MFPs) that control performance. Multiple studies have been conducted to refine MFPs and improve the performance of fuzzy logic systems through the application of a single optimization approach, but since different optimization approaches yield different error terms under different scenarios, the use of a single optimization approach does not necessarily produce truly optimal results. Therefore, this study employed several optimization approaches – ANFIS, GA, and PSO – within a defined search engine unit that compared the error values from the different approaches under different scenarios and, in each scenario, selected the results that had the minimum error value. Additionally , appropriate initial variables for the optimization process were introduced through the Takagi–Sugeno method. This system was applied to a case study of the Diez Lagos (DL) flood controlling system in southern New Mexico, and we found that it had lower average error terms than a single optimization approach in monitoring a flood control gate and pump across a range of scenarios. Overall, using evolutionary algorithms in a novel search engine led to superior performance, using the Takagi–Sugeno method led to near-optimum initial values for the MFPs, and developing a feedback monitoring system consistently led to reliable operating rules. Therefore, we recommend the use of different methods in the search engine unit for finding the optimal MFPs, and selecting the MFPs from the method which has the lowest error value among them.
Decision-making processes in water resources projects are often multi-criteria, in which numerous... more Decision-making processes in water resources projects are often multi-criteria, in which numerous techniques have been developed for evaluation of those projects. The main concern in utilizing multi-criteria decision-making (MCDM) techniques is that, di↵erent techniques may result di↵erent outputs, therefore, selecting an appropriate technique is crucially important. Most decision makers prefer simple and transparent decision-making approaches which simultaneously show the trade-o↵s among the di↵erent decisions. This study utilizes multiple comparison of MCDM techniques to interpret the similarities and dissimilarities of those methods and their consequences in the same project which is multi-criteria management of stochastic floods in the Sunland Park area (Diez Lagos) in southern New Mexico. The objectives of the Diez Lagos flood control system are flood damage reduction (FDR), increasing usable water supply (WS) from stochastic floods, E. coli remediation (ER) from storm water , riparian habitat restoration (RHR), and Human health and safety (HHS) in the study area. For all techniques, we simulated the same decision in the form of decision matrix with m alternatives (flood control rules) against n criteria (FDR, WS, ER, RHR, HHS and related Costs). We investigate six techniques: TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (in Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje), SAW (Simple Additive Weights), AHP (Analytic Hierarchy Process), ELEC-TRE (Elimination et Choice Translating Reality), and Compromise Programming (CP). The evaluation of numerical results of this study can lead to selecting the best decisionmaking technique which can be extended to the other projects.
8:30-9:30 (60') Registration 11:30-13:00 (90') Lunch/ Security check for p.m. session 14:45-15:00... more 8:30-9:30 (60') Registration 11:30-13:00 (90') Lunch/ Security check for p.m. session 14:45-15:00 (15') Break (Change the stage) 16:15-16:30 (15') Coffee Break 17:30-17:35 (5') Break (Change the stage) Abstract Code 294 FLOOD RISK MANAGEMENT PLANS IN EUROPE: EXPERIENCES WITH THE PREPARATION AND IMPLEMENTATION Jos van Alphen, Abstract Code 394 ROBUST RIVER MANAGEMENT IN THE NETHERLANDS: THE ROUTE TO 2100 AND BEYOND Ralph Schielen
To reach social and economic sustainability in arid and semi-arid areas under water scarce situat... more To reach social and economic sustainability in arid and semi-arid areas under water scarce situations, it is important to promote efficient use of water through improved management of water resources. This paper develops a nonlinear discrete-time dynamic model to describe the operation of a single-purpose reservoir during the irrigation season. The model hybridizes the dynamics associated with the water released from a reservoir to the actual water used by crops at farm level. The impact on crop yield because of water deficit and the effect of soil moisture dynamics on crop water requirements are considered by an integrated soil water balance model. As the developed model is a nonlinear one, it is solved using a new global optimization algorithm, namely particle swarm optimization (PSO). The model provides an irrigation time interval in each growth stage and determines the optimal distribution of area, the water to each crop and the total farm income. The model's applicability is illustrated through a case study of Mirzakhanlu Reservoir system in Northwestern Iran. Also, the consideration of economic benefits in the objective function and its effect on the water allocation decisions for crops are studied. Thus, the output from the model contains initial storages, releases, overflows and evaporation losses for each 10-day period on the reservoir side; and allocation of water, actual evapotranspiration and initial soil moisture for each crop for each 10-day period on the field side, therefore facilitating decision making for optimal utilization of the available water resources 1 . . Bazargan has been involved in many projects in area of reservoir operation, flood controlling dam optimization, development of smart hydraulic structures, sediment, transportation through Porous Media, hydraulic structures design, marine structures design, ground water flow simulation, and flow through open channel. He has published more than 30 articles in high quality journals and international conference proceedings.
Flow through rock-fill material can be classified as laminar, transitional and turbulent. Forchhe... more Flow through rock-fill material can be classified as laminar, transitional and turbulent. Forchheimer model that is derived using dimensional analysis and Navier-Stokes equations has acceptable accuracy for all three types of flow. A number of researchers presented their models based on different models of Reynolds Number and Darcy-Weisbach friction coefficient. While in perfect turbulent flow, the effect of Reynolds number on Darcy Weisbach coefficient is negligible, and, the effect of Reynolds number in all these models is only significant on laminar type flow and to some extend in transitional type flow. In all the above mentioned models the flow is based on physical properties of rock-fill materials. But, at the present work a new model is being presented which can be used for analyzing triplet flow types through rock fill materials. This model is based on a new dimensionless number which will be described below. 1
JOURNAL OF WATER …, 2011
According to the former researcher's presented relations for flow through rock-fill porous media,... more According to the former researcher's presented relations for flow through rock-fill porous media, the effects of physical characteristics was not studied separately. Hence, due to the application of these relations, physical characteristics of porous materials effect must be investigated separately. In various constructed physical models of porous media, the effect of several variables such as unified coefficient of materials, kind of materials, material gradation and material figures on the parameters such as void ratio, raggedness and specified surface area of materials have been studied. These parameters have a significant effect on the flow discharge coefficient through rock-fill porous materials. In the defined research during this paper, using artificial unique spherical materials with diameters of 10, 37 and 75 mm with the same configuration, the effect of parameters including specified area of rock-fill materials, figure of rock-fill materials and the size of rock-fill materials on the hydraulic characteristics of discharge flow through rock-fill porous media in rock-fill dam models with inbuilt spillway and spillways on the upstream face, have been studied. Also, using the data of these experiments in combination with the data of experiments of the former researches on the natural rock-fill materials, dimensionless relations of Bazargan have been investigated. Finally, using statistical multi variable analysis, a dimensionless relation with maximum correlation coefficient and acceptable accuracy based on the physical characteristics has been presented.
This study assessed the performances of the traditional temperature-index snowmelt runoff model (... more This study assessed the performances of the traditional temperature-index snowmelt runoff model (SRM) and an SRM model with a finer zonation based on aspect and slope (SRM þ AS model) in a data-scarce mountain watershed in the Urumqi River Basin, in Northwest China. The proposed SRM þ AS model was used to estimate the melt rate with the degree-day factor (DDF) through the division of watershed elevation zones based on aspect and slope. The simulation results of the SRM þ AS model were compared with those of the traditional SRM model to identify the improvements of the SRM þ AS model's performance with consideration of topographic features of the watershed. The results show that the performance of the SRM þ AS model has improved slightly compared to that of the SRM model. The coefficients of determination increased from 0.73, 0.69, and 0.79 with the SRM model to 0.76, 0.76, and 0.81 with the SRM þ AS model during the simulation and validation periods in 2005, 2006, and 2007, respectively. The proposed SRM þ AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization, careful input data selection, and data preparation.
Since fresh water is limited while agricultural and human water demands are continuously increasi... more Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models – autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) – were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predic-tors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.
Since fresh water is limited while agricultural and human water demands are continuously increasi... more Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models – autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) – were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predic-tors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.
Since fresh water is limited while agricultural and human water demands are continuously increasi... more Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models – autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) – were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predic-tors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.
This study assessed the performances of the traditional temperature-index snowmelt runoff model (... more This study assessed the performances of the traditional temperature-index snowmelt runoff model (SRM) and an SRM model with a finer zonation based on aspect and slope (SRM þ AS model) in a data-scarce mountain watershed in the Urumqi River Basin, in Northwest China. The proposed SRM þ AS model was used to estimate the melt rate with the degree-day factor (DDF) through the division of watershed elevation zones based on aspect and slope. The simulation results of the SRM þ AS model were compared with those of the traditional SRM model to identify the improvements of the SRM þ AS model's performance with consideration of topographic features of the watershed. The results show that the performance of the SRM þ AS model has improved slightly compared to that of the SRM model. The coefficients of determination increased from 0.73, 0.69, and 0.79 with the SRM model to 0.76, 0.76, and 0.81 with the SRM þ AS model during the simulation and validation periods in 2005, 2006, and 2007, respectively. The proposed SRM þ AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization, careful input data selection, and data preparation.
Decision-making processes in water resources projects are often multi-criteria, and numerous tech... more Decision-making processes in water resources projects are often multi-criteria, and numerous techniques have been developed for evaluating these projects. The main concern in utilizing multi-criteria decision-making (MCDM) techniques is that different techniques may result different outputs, therefore, selecting an appropriate technique is crucially important. Most decision makers prefer simple and transparent decision-making approaches which simultaneously show the trade-offs among the different decisions. This study utilizes multiple comparisons of MCDM techniques to interpret the similarities and dissimilarities of those methods and their consequences in the same project, which is multi-criteria management of stochastic floods in the Sunland Park area (Diez Lagos) in southern New Mexico. The objectives of the Diez Lagos flood control system are flood damage reduction (FDR), increasing usable water supply (WS) from stochastic floods, E. coli remediation (ER) from storm water, riparian habitat restoration (RHR), and human health and safety (HHS) in the study area. For all techniques, we simulated the same decision in the form of a decision matrix with m alternatives (flood control rules) against n criteria (FDR, WS, ER, RHR, HHS, and related Costs). We investigate six techniques: TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (in Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje), SAW (Simple Additive Weights), AHP (Analytic Hierarchy Process), ELECTRE (Elimination et Choice Translating Reality), and Compromise Programming (CP). The evaluation of the numerical results from this study can lead to the selection of the best decision-making technique, which can be extended to other projects.
Different multi-criteria decision-making (MCDM) techniques require different levels of computatio... more Different multi-criteria decision-making (MCDM) techniques require different levels of computational intensity and may produce different outputs, so selecting an appropriate technique largely determines the quality of the recommended decision and the effort required to obtain that decision. In most real environments , criteria and their constraints are not deterministic and cannot be specified precisely; therefore, those criteria are uncertain or fuzzy. To facilitate the selection of an appropriate MCDM method under a fuzzy environment, this study investigates and statistically compares the performances of ten commonly used MCDM techniques: simple additive weights (SAW), weighted product method (WPM), compromise programming (CP), technique for order preference by similarity to ideal solution (TOPSIS), four types of analytical hierarchy process (AHP), VIKOR (in Serbian: VIseKriterijumska Optimizacija I Kompromisno Re-senje), and ELECTRE (in French: ELimination Et Choix Traduisant la REalité). These techniques' performances were compared using fuzzy criteria and constraints, matching the conditions usually found in real applications. To conduct the comparisons, the 10 multi-criteria decision ranking methods were applied to 1250 simulated sets of decision matrices with fuzzy triangular values, and 12,500 sets of ranks were analyzed to compare the ranking methods. SAW and TOPSIS had statistically similar performances. ELECTRE was not preferable in providing full, sorted ranks among the alternatives. VIKOR considering its ranking process, for specific conditions, assigns identical ranks for several alternatives; when full, sorted ranks are required, VIKOR is unfavorable, although it is a powerful technique in introducing the closest alternative to the ideal condition. Types 1 and 3 of AHP and types 2 and 4 of AHP had close performances. Notably, no ranking method was significantly sensitive to uncertainty levels when uncertainty changed symmetrically.
In fuzzy logic controllers (FLCs), optimal performance can be defined as performance that minimiz... more In fuzzy logic controllers (FLCs), optimal performance can be defined as performance that minimizes the deviation (error term) between the decisions of the fuzzy logic systems and the decisions of experts. A range of approaches – such as genetic algorithms (GA), particle swarm optimization (PSO), artificial neural networks (ANN), and adaptive network based fuzzy inference systems (ANFIS) – can be used to pursue optimal performance for FLCs by refining the membership function parameters (MFPs) that control performance. Multiple studies have been conducted to refine MFPs and improve the performance of fuzzy logic systems through the application of a single optimization approach, but since different optimization approaches yield different error terms under different scenarios, the use of a single optimization approach does not necessarily produce truly optimal results. Therefore, this study employed several optimization approaches – ANFIS, GA, and PSO – within a defined search engine unit that compared the error values from the different approaches under different scenarios and, in each scenario, selected the results that had the minimum error value. Additionally , appropriate initial variables for the optimization process were introduced through the Takagi–Sugeno method. This system was applied to a case study of the Diez Lagos (DL) flood controlling system in southern New Mexico, and we found that it had lower average error terms than a single optimization approach in monitoring a flood control gate and pump across a range of scenarios. Overall, using evolutionary algorithms in a novel search engine led to superior performance, using the Takagi–Sugeno method led to near-optimum initial values for the MFPs, and developing a feedback monitoring system consistently led to reliable operating rules. Therefore, we recommend the use of different methods in the search engine unit for finding the optimal MFPs, and selecting the MFPs from the method which has the lowest error value among them.
Decision-making processes in water resources projects are often multi-criteria, in which numerous... more Decision-making processes in water resources projects are often multi-criteria, in which numerous techniques have been developed for evaluation of those projects. The main concern in utilizing multi-criteria decision-making (MCDM) techniques is that, di↵erent techniques may result di↵erent outputs, therefore, selecting an appropriate technique is crucially important. Most decision makers prefer simple and transparent decision-making approaches which simultaneously show the trade-o↵s among the di↵erent decisions. This study utilizes multiple comparison of MCDM techniques to interpret the similarities and dissimilarities of those methods and their consequences in the same project which is multi-criteria management of stochastic floods in the Sunland Park area (Diez Lagos) in southern New Mexico. The objectives of the Diez Lagos flood control system are flood damage reduction (FDR), increasing usable water supply (WS) from stochastic floods, E. coli remediation (ER) from storm water , riparian habitat restoration (RHR), and Human health and safety (HHS) in the study area. For all techniques, we simulated the same decision in the form of decision matrix with m alternatives (flood control rules) against n criteria (FDR, WS, ER, RHR, HHS and related Costs). We investigate six techniques: TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (in Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje), SAW (Simple Additive Weights), AHP (Analytic Hierarchy Process), ELEC-TRE (Elimination et Choice Translating Reality), and Compromise Programming (CP). The evaluation of numerical results of this study can lead to selecting the best decisionmaking technique which can be extended to the other projects.
8:30-9:30 (60') Registration 11:30-13:00 (90') Lunch/ Security check for p.m. session 14:45-15:00... more 8:30-9:30 (60') Registration 11:30-13:00 (90') Lunch/ Security check for p.m. session 14:45-15:00 (15') Break (Change the stage) 16:15-16:30 (15') Coffee Break 17:30-17:35 (5') Break (Change the stage) Abstract Code 294 FLOOD RISK MANAGEMENT PLANS IN EUROPE: EXPERIENCES WITH THE PREPARATION AND IMPLEMENTATION Jos van Alphen, Abstract Code 394 ROBUST RIVER MANAGEMENT IN THE NETHERLANDS: THE ROUTE TO 2100 AND BEYOND Ralph Schielen
To reach social and economic sustainability in arid and semi-arid areas under water scarce situat... more To reach social and economic sustainability in arid and semi-arid areas under water scarce situations, it is important to promote efficient use of water through improved management of water resources. This paper develops a nonlinear discrete-time dynamic model to describe the operation of a single-purpose reservoir during the irrigation season. The model hybridizes the dynamics associated with the water released from a reservoir to the actual water used by crops at farm level. The impact on crop yield because of water deficit and the effect of soil moisture dynamics on crop water requirements are considered by an integrated soil water balance model. As the developed model is a nonlinear one, it is solved using a new global optimization algorithm, namely particle swarm optimization (PSO). The model provides an irrigation time interval in each growth stage and determines the optimal distribution of area, the water to each crop and the total farm income. The model's applicability is illustrated through a case study of Mirzakhanlu Reservoir system in Northwestern Iran. Also, the consideration of economic benefits in the objective function and its effect on the water allocation decisions for crops are studied. Thus, the output from the model contains initial storages, releases, overflows and evaporation losses for each 10-day period on the reservoir side; and allocation of water, actual evapotranspiration and initial soil moisture for each crop for each 10-day period on the field side, therefore facilitating decision making for optimal utilization of the available water resources 1 . . Bazargan has been involved in many projects in area of reservoir operation, flood controlling dam optimization, development of smart hydraulic structures, sediment, transportation through Porous Media, hydraulic structures design, marine structures design, ground water flow simulation, and flow through open channel. He has published more than 30 articles in high quality journals and international conference proceedings.
Flow through rock-fill material can be classified as laminar, transitional and turbulent. Forchhe... more Flow through rock-fill material can be classified as laminar, transitional and turbulent. Forchheimer model that is derived using dimensional analysis and Navier-Stokes equations has acceptable accuracy for all three types of flow. A number of researchers presented their models based on different models of Reynolds Number and Darcy-Weisbach friction coefficient. While in perfect turbulent flow, the effect of Reynolds number on Darcy Weisbach coefficient is negligible, and, the effect of Reynolds number in all these models is only significant on laminar type flow and to some extend in transitional type flow. In all the above mentioned models the flow is based on physical properties of rock-fill materials. But, at the present work a new model is being presented which can be used for analyzing triplet flow types through rock fill materials. This model is based on a new dimensionless number which will be described below. 1
JOURNAL OF WATER …, 2011
According to the former researcher's presented relations for flow through rock-fill porous media,... more According to the former researcher's presented relations for flow through rock-fill porous media, the effects of physical characteristics was not studied separately. Hence, due to the application of these relations, physical characteristics of porous materials effect must be investigated separately. In various constructed physical models of porous media, the effect of several variables such as unified coefficient of materials, kind of materials, material gradation and material figures on the parameters such as void ratio, raggedness and specified surface area of materials have been studied. These parameters have a significant effect on the flow discharge coefficient through rock-fill porous materials. In the defined research during this paper, using artificial unique spherical materials with diameters of 10, 37 and 75 mm with the same configuration, the effect of parameters including specified area of rock-fill materials, figure of rock-fill materials and the size of rock-fill materials on the hydraulic characteristics of discharge flow through rock-fill porous media in rock-fill dam models with inbuilt spillway and spillways on the upstream face, have been studied. Also, using the data of these experiments in combination with the data of experiments of the former researches on the natural rock-fill materials, dimensionless relations of Bazargan have been investigated. Finally, using statistical multi variable analysis, a dimensionless relation with maximum correlation coefficient and acceptable accuracy based on the physical characteristics has been presented.