The Experts below are selected from a list of 37062 Experts worldwide ranked by ideXlab platform
Alireza Daneshi - One of the best experts on this subject based on the ideXlab platform.
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application of support vector machine random forest and genetic algorithm optimized random forest models in groundwater potential mapping
Water Resources Management, 2017Co-Authors: Seyed Amir Naghibi, Kourosh Ahmadi, Alireza DaneshiAbstract:Abstract Regarding the ever increasing issue of water scarcity in different countries, the current study plans to apply support vector machine (SVM), random forest (RF), and genetic algorithm optimized random forest (RFGA) methods to assess groundwater potential by spring locations. To this end, 14 effective variables including DEM-derived, river-Based, fault-Based, land use, and lithology factors were provided. Of 842 spring locations found, 70% (589) were implemented for model training, and the rest of them were used to evaluate the models. The mentioned models were run and groundwater potential maps (GPMs) were produced. At last, receiver operating characteristics (ROC) curve was plotted to evaluate the efficiency of the methods. The results of the current study denoted that RFGA, and RF methods had better efficacy than different kernels of SVM model. Area under curve (AUC) of ROC value for RF and RFGA was estimated as 84.6, and 85.6%, respectively. AUC of ROC was computed as SVM- linear (78.6%), SVM-polynomial (76.8%), SVM-sigmoid (77.1%), and SVM- Radial Based Function (77%). Furthermore, the results represented higher importance of altitude, TWI, and slope angle in groundwater assessment. The methodology created in the current study could be transferred to other places with water scarcity issues for groundwater potential assessment and management.
Arun Goel - One of the best experts on this subject based on the ideXlab platform.
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estimation of discharge and end depth in trapezoidal channel by support vector machines
Water Resources Management, 2007Co-Authors: Mahesh Pal, Arun GoelAbstract:This paper presents the results of an application of support vector machines Based modelling technique (Radial Based kernel and polynomial kernel) to determine discharge and end-depth of a free overfall occurring over a smooth trapezoidal channel with positive, horizontal or zero and negative bottom slopes. The data used in this study are taken from the earlier published work reported in the literature (Ahmad 2001). The results of the study indicate that the Radial Based Function and polynomial kernels support vector machines modelling technique can be used effectively for predicting the discharge and the end depth for a trapezoidal shaped channel with different slopes as compared to the empirical relations suggested by Ahmad (2001); Gupta et al. (1993) and a back propagation neural network technique. The predicted values of both discharge and end depth compared well to the results obtained by using empirical relations derived in previous studies as well as with a back propagation neural network model. In case of discharge prediction, correlation coefficient was more than 0.995 with all three different slopes, while it was more than 0.996 in predicting the end depth using Radial Based kernel of support vector machines algorithm. Thus, suggesting the application and usefulness of this technique in predicting the discharge as well as end depth in the trapezoidal shaped channel as an alternative to the empirical relations and neural network algorithm. Further, a smaller computational time is an added advantage of using support vector machines in comparison to the neural network classifier, as observed in the present study.
Seyed Amir Naghibi - One of the best experts on this subject based on the ideXlab platform.
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application of support vector machine random forest and genetic algorithm optimized random forest models in groundwater potential mapping
Water Resources Management, 2017Co-Authors: Seyed Amir Naghibi, Kourosh Ahmadi, Alireza DaneshiAbstract:Abstract Regarding the ever increasing issue of water scarcity in different countries, the current study plans to apply support vector machine (SVM), random forest (RF), and genetic algorithm optimized random forest (RFGA) methods to assess groundwater potential by spring locations. To this end, 14 effective variables including DEM-derived, river-Based, fault-Based, land use, and lithology factors were provided. Of 842 spring locations found, 70% (589) were implemented for model training, and the rest of them were used to evaluate the models. The mentioned models were run and groundwater potential maps (GPMs) were produced. At last, receiver operating characteristics (ROC) curve was plotted to evaluate the efficiency of the methods. The results of the current study denoted that RFGA, and RF methods had better efficacy than different kernels of SVM model. Area under curve (AUC) of ROC value for RF and RFGA was estimated as 84.6, and 85.6%, respectively. AUC of ROC was computed as SVM- linear (78.6%), SVM-polynomial (76.8%), SVM-sigmoid (77.1%), and SVM- Radial Based Function (77%). Furthermore, the results represented higher importance of altitude, TWI, and slope angle in groundwater assessment. The methodology created in the current study could be transferred to other places with water scarcity issues for groundwater potential assessment and management.
Seyed Abbas Hosseini - One of the best experts on this subject based on the ideXlab platform.
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Prediction of bed load sediments using different artificial neural network models
Frontiers of Structural and Civil Engineering, 2020Co-Authors: Reza Asheghi, Seyed Abbas HosseiniAbstract:Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, Radial Based Function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.
Kourosh Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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application of support vector machine random forest and genetic algorithm optimized random forest models in groundwater potential mapping
Water Resources Management, 2017Co-Authors: Seyed Amir Naghibi, Kourosh Ahmadi, Alireza DaneshiAbstract:Abstract Regarding the ever increasing issue of water scarcity in different countries, the current study plans to apply support vector machine (SVM), random forest (RF), and genetic algorithm optimized random forest (RFGA) methods to assess groundwater potential by spring locations. To this end, 14 effective variables including DEM-derived, river-Based, fault-Based, land use, and lithology factors were provided. Of 842 spring locations found, 70% (589) were implemented for model training, and the rest of them were used to evaluate the models. The mentioned models were run and groundwater potential maps (GPMs) were produced. At last, receiver operating characteristics (ROC) curve was plotted to evaluate the efficiency of the methods. The results of the current study denoted that RFGA, and RF methods had better efficacy than different kernels of SVM model. Area under curve (AUC) of ROC value for RF and RFGA was estimated as 84.6, and 85.6%, respectively. AUC of ROC was computed as SVM- linear (78.6%), SVM-polynomial (76.8%), SVM-sigmoid (77.1%), and SVM- Radial Based Function (77%). Furthermore, the results represented higher importance of altitude, TWI, and slope angle in groundwater assessment. The methodology created in the current study could be transferred to other places with water scarcity issues for groundwater potential assessment and management.
