Prediction of wave height is of great importance in marine and coastal engineering. Soft computin... more Prediction of wave height is of great importance in marine and coastal engineering. Soft computing tools such as artificial neural networks (ANNs) are recently used for prediction of significant wave height. However, ANNs are not as transparent as semi-empirical regression-based models. In addition, neural networks approach needs to find network parameters such as number of hidden layers and neurons by trial and error, which is time consuming. Therefore, in this work, model trees as a new soft computing method was invoked for prediction of significant wave height. The main advantage of model trees is that, compared to neural networks, they represent understandable rules. These rules can be readily expressed so that humans can understand them. The data set used for developing model trees comprises of wind and wave data gathered in Lake Superior from . M5 0 algorithm was employed for building and evaluating model trees. Training and testing data include wind speed (U 10 ) as the input variable and the significant wave height (H s ) as the output variable. Results indicate that error statistics of model trees and feed-forward back propagation (FFBP) ANNs were similar, while model trees was marginally more accurate. In addition, model tree shows that for wind speed above 4.7 m/s, the wave height increases nonlinearly by the wind speed.
Hindcasting of wave parameters is necessary for many applications in coastal and offshore enginee... more Hindcasting of wave parameters is necessary for many applications in coastal and offshore engineering and is generally made with the help of sophisticated numerical models. This paper presents alternative hindcast models based on Artificial Neural Networks (ANNs), Fuzzy Inference System (FIS) and Adaptive-Network-based Fuzzy Inference System (ANFIS). The data set used in this study comprises wave and wind data gathered from deep water location in Lake Ontario. Wind speed, wind direction, fetch length and wind duration were used as input variables, while significant wave height, peak spectral period and mean wave direction were the output parameters. Different topologies of ANNs were considered to predict the wave parameters and the relative importance of input parameters were determined. Finally, the results of ANNs-based models, FIS-and ANFIS-based models were compared. Results indicated that error statistics of soft computing models were similar, while ANFIS models were marginally...
In recent years, much attention has been paid to the optimal design of pipeline systems. In this ... more In recent years, much attention has been paid to the optimal design of pipeline systems. In this study, the problem of pipeline system optimal design has been solved through genetic algorithm and mathematical optimization. Pipe diameters and their thicknesses are considered as decision variables to be designed in a manner that water column separation and excessive pressures are avoided in the event of pump failure. Capabilities of the genetic algorithm and the mathematical programming method are compared for the problem under consideration. For simulation of transient streams, explicit characteristic method is used in which devices such as pumps are defined as boundary conditions of the equations defining the hydraulic behavior of pipe segments. The problem of optimal design of pipeline systems is a constrained problem which is converted to an unconstrained optimization problem using an external penalty function approach. The efficiency of the proposed approaches is verified in one ...
Prediction of scour around a pile due to oscillatory wave action is very important in many offsho... more Prediction of scour around a pile due to oscillatory wave action is very important in many offshore and marine engineering problems. Because of complexity of scour process, most of the empirical formulas are unable to estimate scour hole depth accurately. Artificial Neural Networks (ANNs) and regression trees are efficient procedures to understand and model complex systems with ambiguous relations. A Multi Layer Perceptron (MLP) is one of the most common kinds of ANNs and has been used to map input-output systems while CART algorithm was employed for building and evaluating regression trees. In the present study, two input sets were employed to estimate scour depth: (a) dimensional parameters such as bed grain size, pile diameter, wave period, wave height, maximum flow velocity and maximum shear velocity (b) nondimensional parameters such as pile Reynolds number, Shields parameter, Keulegan- Carpenter number, grain Reynolds number, sediment number and relative density. Output parame...
In recent years, much attention has been paid to the optimal design of pipeline systems. In this ... more In recent years, much attention has been paid to the optimal design of pipeline systems. In this study, the problem of pipeline system optimal design has been solved through genetic algorithm and mathematical optimization. Pipe diameters and their thicknesses are considered as decision variables to be designed in a manner that water column separation and excessive pressures are avoided in the event of pump failure. Capabilities of the genetic algorithm and the mathematical programming method are compared for the problem under consideration. For simulation of transient streams, explicit characteristic method is used in which devices such as pumps are defined as boundary conditions of the equations defining the hydraulic behavior of pipe segments. The problem of optimal design of pipeline systems is a constrained problem which is converted to an unconstrained optimization problem using an external penalty function approach. The efficiency of the proposed approaches is verified in one ...
International Journal of Computers and Applications, 2011
... [7] SK Shevade, SS Keerthi, C. Bhattacharyya, & ... He is currently t... more ... [7] SK Shevade, SS Keerthi, C. Bhattacharyya, & ... He is currently the Deputy Direc-tor of REDAC, Universiti Sains Malaysia. Javad Mahjoobi was born in Dargaz, Iran. He obtained his first degree in BS (Civil Engineering) from Islamic Azad University. He later pursued his M.Sc. ...
ABSTRACT One of the most important factors in bridge destruction is scour around piers of bridges... more ABSTRACT One of the most important factors in bridge destruction is scour around piers of bridges. The scour depth evaluation around the bridge pier is very critical in bridge design. Although there are several empirical formulas to predict depth scour, these formulas usually have not had accurate results. Therefore, there has been an increase in use of soft computing tools such as neural networks (ANNs), fuzzy inference system (FIS) and adaptive network fuzzy inference system (ANFIS) for this purpose. Model trees are one of the prevailing data mining tools. In the present study, for prediction of scour depth, model trees (M5′ algorithm) and regression trees (CART algorithm) with using experimental data of scour measurement in clear water condition were employed. Moreover, these models were used for two cases; the first one with original (dimensional) data and the second one with non‐dimensional data and the results were compared with the results of six empirical formulas. The results showed that model and regression trees are more efficient than the empirical formulas to predict scour depth.
ABSTRACT Significant wave height estimates are necessary for many applications in coastal and off... more ABSTRACT Significant wave height estimates are necessary for many applications in coastal and offshore engineering and therefore various prediction models have been proposed in the literature for this purpose. In this study, the performances of Decision trees classification for prediction wave parameters were investigated. The data set used in this study comprises of wave data and over water wind data gathered from deep water location in Lake Ontario. The data set was divided into two groups. The first one that comprises of 26 days wind and wave measurement was used as training and checking data to develop tree models. The second one that comprises of 14 days wind and wave measurement was used to verify the models. Training and testing data include wind speed, wind direction, fetch length and wind duration as input variables and significant wave heights as output variable. The wave heights for whole data set are grouped into wave height bins of 0.25 m. Then a class is assigned to each bin. For evaluation of the developed model the mean of each class is compared with the observed data.
Wave parameters prediction is an important issue in coastal and offshore engineering. In this lit... more Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to predict wave parameters, as well. It is not a long time that support vector machine (SVM) is introduced as a strong machine learning and data mining tool. In this paper, it is used to predict significant wave height (H s ). The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan. Current wind speed (u) and those belonging up to six previous hours are given as input variables, while the significant wave height is the output parameter. The SVM results are compared with those of artificial neural networks, multi-layer perceptron (MLP) and radial basis function (RBF) models. The results show that SVM can be successfully used for prediction of H s . Furthermore, comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required.
In this study, the performances of classification and regression trees for the prediction of sign... more In this study, the performances of classification and regression trees for the prediction of significant wave heights were investigated. The data set used in this study is comprised of 5 years of wave and wind data gathered from a deep water location in Lake Michigan. Training and testing data include wind speed and wind direction as the input variables and significant wave heights (H s ) as the output variable. To build the classification trees, a C5 algorithm was invoked. Then, significant wave heights for the whole data set were grouped into wave height bins of 0.25 m and a class was assigned to each bin. For evaluation of the developed model, the index of each predicted class was compared with that of the observed data. The CART algorithm was employed for building and evaluating regression trees. Results of decision trees were then compared with those of artificial neural networks (ANNs). The error statistics of decision trees and ANNs were nearly similar. Results indicate that the decision tree, as an efficient novel approach with an acceptable range of error, can be used successfully for prediction of H s . It is argued that the advantage of decision trees is that, in contrast to neural networks, they represent rules.
An accurate estimation of scour depth around a pile is very difficult due to the complex behavior... more An accurate estimation of scour depth around a pile is very difficult due to the complex behavior of flow around a pile structure on an erodible bed. In the current study, Regression Trees (RT) and Artificial Neural Networks (ANNs) as remedy data mining approaches are suggested to estimate the scour depth due to regular waves. These approaches were used to
Hindcasting of wave parameters is necessary for many applications in coastal and offshore enginee... more Hindcasting of wave parameters is necessary for many applications in coastal and offshore engineering and is generally made with the help of sophisticated numerical models. This paper presents alternative hindcast models based on Artificial Neural Networks (ANNs), Fuzzy Inference System (FIS) and Adaptive-Network-based Fuzzy Inference System (ANFIS). The data set used in this study comprises wave and wind data gathered
Wind waves play a significant role in ocean and coastal activities. In this study, the performanc... more Wind waves play a significant role in ocean and coastal activities. In this study, the performance of decision trees classification for prediction of wave parameters was investigated. The data set used in this study comprises of wind and wave data gathered in Lake Ontario from October to November, 2004 and further from November to December, 2005. The data set was gathered by National Data Buoy Center (NDBC) in station 45012 at 43˚ 37� 09� N and 77˚ 24� 18��W. The data set was divided into two groups. The first one that comprises of 26 days (611 data points of year 2005) wind and wave measurements was used to train the models. The second one that comprises of 14 days (326 data points of year 2004) wind and wave measurements was used to verify the models. Training and testing data include wind speed, wind direction, fetch length and wind duration as input variables and significant wave height (Hs) and peak spectral period (Tp) as output variables. For building classification trees, C5...
Prediction of wave height is of great importance in marine and coastal engineering. Soft computin... more Prediction of wave height is of great importance in marine and coastal engineering. Soft computing tools such as artificial neural networks (ANNs) are recently used for prediction of significant wave height. However, ANNs are not as transparent as semi-empirical regression-based models. In addition, neural networks approach needs to find network parameters such as number of hidden layers and neurons by trial and error, which is time consuming. Therefore, in this work, model trees as a new soft computing method was invoked for prediction of significant wave height. The main advantage of model trees is that, compared to neural networks, they represent understandable rules. These rules can be readily expressed so that humans can understand them. The data set used for developing model trees comprises of wind and wave data gathered in Lake Superior from . M5 0 algorithm was employed for building and evaluating model trees. Training and testing data include wind speed (U 10 ) as the input variable and the significant wave height (H s ) as the output variable. Results indicate that error statistics of model trees and feed-forward back propagation (FFBP) ANNs were similar, while model trees was marginally more accurate. In addition, model tree shows that for wind speed above 4.7 m/s, the wave height increases nonlinearly by the wind speed.
Hindcasting of wave parameters is necessary for many applications in coastal and offshore enginee... more Hindcasting of wave parameters is necessary for many applications in coastal and offshore engineering and is generally made with the help of sophisticated numerical models. This paper presents alternative hindcast models based on Artificial Neural Networks (ANNs), Fuzzy Inference System (FIS) and Adaptive-Network-based Fuzzy Inference System (ANFIS). The data set used in this study comprises wave and wind data gathered from deep water location in Lake Ontario. Wind speed, wind direction, fetch length and wind duration were used as input variables, while significant wave height, peak spectral period and mean wave direction were the output parameters. Different topologies of ANNs were considered to predict the wave parameters and the relative importance of input parameters were determined. Finally, the results of ANNs-based models, FIS-and ANFIS-based models were compared. Results indicated that error statistics of soft computing models were similar, while ANFIS models were marginally...
In recent years, much attention has been paid to the optimal design of pipeline systems. In this ... more In recent years, much attention has been paid to the optimal design of pipeline systems. In this study, the problem of pipeline system optimal design has been solved through genetic algorithm and mathematical optimization. Pipe diameters and their thicknesses are considered as decision variables to be designed in a manner that water column separation and excessive pressures are avoided in the event of pump failure. Capabilities of the genetic algorithm and the mathematical programming method are compared for the problem under consideration. For simulation of transient streams, explicit characteristic method is used in which devices such as pumps are defined as boundary conditions of the equations defining the hydraulic behavior of pipe segments. The problem of optimal design of pipeline systems is a constrained problem which is converted to an unconstrained optimization problem using an external penalty function approach. The efficiency of the proposed approaches is verified in one ...
Prediction of scour around a pile due to oscillatory wave action is very important in many offsho... more Prediction of scour around a pile due to oscillatory wave action is very important in many offshore and marine engineering problems. Because of complexity of scour process, most of the empirical formulas are unable to estimate scour hole depth accurately. Artificial Neural Networks (ANNs) and regression trees are efficient procedures to understand and model complex systems with ambiguous relations. A Multi Layer Perceptron (MLP) is one of the most common kinds of ANNs and has been used to map input-output systems while CART algorithm was employed for building and evaluating regression trees. In the present study, two input sets were employed to estimate scour depth: (a) dimensional parameters such as bed grain size, pile diameter, wave period, wave height, maximum flow velocity and maximum shear velocity (b) nondimensional parameters such as pile Reynolds number, Shields parameter, Keulegan- Carpenter number, grain Reynolds number, sediment number and relative density. Output parame...
In recent years, much attention has been paid to the optimal design of pipeline systems. In this ... more In recent years, much attention has been paid to the optimal design of pipeline systems. In this study, the problem of pipeline system optimal design has been solved through genetic algorithm and mathematical optimization. Pipe diameters and their thicknesses are considered as decision variables to be designed in a manner that water column separation and excessive pressures are avoided in the event of pump failure. Capabilities of the genetic algorithm and the mathematical programming method are compared for the problem under consideration. For simulation of transient streams, explicit characteristic method is used in which devices such as pumps are defined as boundary conditions of the equations defining the hydraulic behavior of pipe segments. The problem of optimal design of pipeline systems is a constrained problem which is converted to an unconstrained optimization problem using an external penalty function approach. The efficiency of the proposed approaches is verified in one ...
International Journal of Computers and Applications, 2011
... [7] SK Shevade, SS Keerthi, C. Bhattacharyya, & ... He is currently t... more ... [7] SK Shevade, SS Keerthi, C. Bhattacharyya, & ... He is currently the Deputy Direc-tor of REDAC, Universiti Sains Malaysia. Javad Mahjoobi was born in Dargaz, Iran. He obtained his first degree in BS (Civil Engineering) from Islamic Azad University. He later pursued his M.Sc. ...
ABSTRACT One of the most important factors in bridge destruction is scour around piers of bridges... more ABSTRACT One of the most important factors in bridge destruction is scour around piers of bridges. The scour depth evaluation around the bridge pier is very critical in bridge design. Although there are several empirical formulas to predict depth scour, these formulas usually have not had accurate results. Therefore, there has been an increase in use of soft computing tools such as neural networks (ANNs), fuzzy inference system (FIS) and adaptive network fuzzy inference system (ANFIS) for this purpose. Model trees are one of the prevailing data mining tools. In the present study, for prediction of scour depth, model trees (M5′ algorithm) and regression trees (CART algorithm) with using experimental data of scour measurement in clear water condition were employed. Moreover, these models were used for two cases; the first one with original (dimensional) data and the second one with non‐dimensional data and the results were compared with the results of six empirical formulas. The results showed that model and regression trees are more efficient than the empirical formulas to predict scour depth.
ABSTRACT Significant wave height estimates are necessary for many applications in coastal and off... more ABSTRACT Significant wave height estimates are necessary for many applications in coastal and offshore engineering and therefore various prediction models have been proposed in the literature for this purpose. In this study, the performances of Decision trees classification for prediction wave parameters were investigated. The data set used in this study comprises of wave data and over water wind data gathered from deep water location in Lake Ontario. The data set was divided into two groups. The first one that comprises of 26 days wind and wave measurement was used as training and checking data to develop tree models. The second one that comprises of 14 days wind and wave measurement was used to verify the models. Training and testing data include wind speed, wind direction, fetch length and wind duration as input variables and significant wave heights as output variable. The wave heights for whole data set are grouped into wave height bins of 0.25 m. Then a class is assigned to each bin. For evaluation of the developed model the mean of each class is compared with the observed data.
Wave parameters prediction is an important issue in coastal and offshore engineering. In this lit... more Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to predict wave parameters, as well. It is not a long time that support vector machine (SVM) is introduced as a strong machine learning and data mining tool. In this paper, it is used to predict significant wave height (H s ). The data set used in this study comprises wave wind data gathered from deep water locations in Lake Michigan. Current wind speed (u) and those belonging up to six previous hours are given as input variables, while the significant wave height is the output parameter. The SVM results are compared with those of artificial neural networks, multi-layer perceptron (MLP) and radial basis function (RBF) models. The results show that SVM can be successfully used for prediction of H s . Furthermore, comparisons indicate that the error statistics of SVM model marginally outperforms ANN even with much less computational time required.
In this study, the performances of classification and regression trees for the prediction of sign... more In this study, the performances of classification and regression trees for the prediction of significant wave heights were investigated. The data set used in this study is comprised of 5 years of wave and wind data gathered from a deep water location in Lake Michigan. Training and testing data include wind speed and wind direction as the input variables and significant wave heights (H s ) as the output variable. To build the classification trees, a C5 algorithm was invoked. Then, significant wave heights for the whole data set were grouped into wave height bins of 0.25 m and a class was assigned to each bin. For evaluation of the developed model, the index of each predicted class was compared with that of the observed data. The CART algorithm was employed for building and evaluating regression trees. Results of decision trees were then compared with those of artificial neural networks (ANNs). The error statistics of decision trees and ANNs were nearly similar. Results indicate that the decision tree, as an efficient novel approach with an acceptable range of error, can be used successfully for prediction of H s . It is argued that the advantage of decision trees is that, in contrast to neural networks, they represent rules.
An accurate estimation of scour depth around a pile is very difficult due to the complex behavior... more An accurate estimation of scour depth around a pile is very difficult due to the complex behavior of flow around a pile structure on an erodible bed. In the current study, Regression Trees (RT) and Artificial Neural Networks (ANNs) as remedy data mining approaches are suggested to estimate the scour depth due to regular waves. These approaches were used to
Hindcasting of wave parameters is necessary for many applications in coastal and offshore enginee... more Hindcasting of wave parameters is necessary for many applications in coastal and offshore engineering and is generally made with the help of sophisticated numerical models. This paper presents alternative hindcast models based on Artificial Neural Networks (ANNs), Fuzzy Inference System (FIS) and Adaptive-Network-based Fuzzy Inference System (ANFIS). The data set used in this study comprises wave and wind data gathered
Wind waves play a significant role in ocean and coastal activities. In this study, the performanc... more Wind waves play a significant role in ocean and coastal activities. In this study, the performance of decision trees classification for prediction of wave parameters was investigated. The data set used in this study comprises of wind and wave data gathered in Lake Ontario from October to November, 2004 and further from November to December, 2005. The data set was gathered by National Data Buoy Center (NDBC) in station 45012 at 43˚ 37� 09� N and 77˚ 24� 18��W. The data set was divided into two groups. The first one that comprises of 26 days (611 data points of year 2005) wind and wave measurements was used to train the models. The second one that comprises of 14 days (326 data points of year 2004) wind and wave measurements was used to verify the models. Training and testing data include wind speed, wind direction, fetch length and wind duration as input variables and significant wave height (Hs) and peak spectral period (Tp) as output variables. For building classification trees, C5...
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