Abutment scour in clear-water and live-bed conditions by GMDH network

2013 ◽  
Vol 67 (5) ◽  
pp. 1121-1128 ◽  
Author(s):  
Mohammad Najafzadeh ◽  
Gholam-Abbas Barani ◽  
Masoud Reza Hessami Kermani
Keyword(s):  
Back Propagation ◽  
Support Vector ◽  
Group Method ◽  
Scour Depth ◽  
Back Propagation Algorithm ◽  
Clear Water ◽  
Sediment Size ◽  
Svm Model ◽  
Vector Machines ◽  
Abutment Scour

In the present study, the Group Method of Data Handling (GMDH) network has been utilized to predict abutments scour depth for both clear-water and live-bed conditions. The GMDH network was developed using a Back Propagation algorithm (BP). Input parameters that were considered as effective variables on abutment scour depth included properties of sediment size, geometry of bridge abutments, and properties of approaching flow. Training and testing performances of the GMDH network were carried out using dimensionless parameters that were collected from the literature. The testing results were compared with those obtained using the Support Vector Machines (SVM) model and the traditional equations. The GMDH network predicted the abutment scour depth with lower error (RMSE (root mean square error) = 0.29 and MAPE (mean absolute percentage of error) = 0.99) and higher (R = 0.98) accuracy than those performed using the SVM model and the traditional equations.

scholarly journals Prediction of pile group scour in waves using support vector machines and ANN

2010 ◽  
Vol 13 (4) ◽  
pp. 609-620 ◽  
Author(s):  
Samaneh Ghazanfari-Hashemi ◽  
Amir Etemad-Shahidi ◽  
Mohammad H. Kazeminezhad ◽  
Amir Reza Mansoori
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Back Propagation ◽  
Pile Group ◽  
Diameter Ratio ◽  
Support Vector ◽  
Scour Depth ◽  
Empirical Methods ◽  
Pile Groups ◽  
Vector Machines

Scour around pile groups is rather complicated and not yet fully understood due to the fact that it arises from the triple interaction of fluid–structure–seabed. In this study, two data mining approaches, i.e. Support Vector Machines (SVM) and Artificial Neural Networks (ANN), were applied to estimate the wave-induced scour depth around pile groups. To consider various arrangements of pile groups in the development of the models, datasets collected in the field and laboratory studies were used and arrangement parameters were considered in the models. Several non-dimensional controlling parameters, including the Keulegan–Carpenter number, pile Reynolds number, Shield's parameter, sediment number, gap to diameter ratio and number of piles were used as the inputs. Performances of the developed SVM and ANN models were compared with those of existing empirical methods. Results indicate that the data mining approaches used outperform empirical methods in terms of accuracy. They also indicate that SVM will provide a better estimation of scour depth than ANN (back-propagation/multi-layer perceptron). Sensitivity analysis was also carried out to investigate the relative importance of non-dimensional parameters. It was found that the Keulegan–Carpenter number and gap to diameter ratio have the greatest effect on the equilibrium scour depth around pile groups.

scholarly journals A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 301 ◽  
Author(s):  
Hossein Bonakdari ◽  
Fatemeh Moradi ◽  
Isa Ebtehaj ◽  
Bahram Gharabaghi ◽  
Ahmed A. Sattar ◽  
...  
Keyword(s):  
Structural Integrity ◽  
Fundamental Equation ◽  
Percentage Error ◽  
Scour Depth ◽  
Clear Water ◽  
Dimensionless Variable ◽  
Sediment Size ◽  
Wide Range ◽  
Abutment Scour ◽  
The Impact

Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

scholarly journals Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers

2021 ◽  
Author(s):  
B. M. Sreedhara ◽  
Amit Prakash Patil ◽  
Jagalingam Pushparaj ◽  
Geetha Kuntoji ◽  
Sujay Raghavendra Naganna
Keyword(s):  
Model Performance ◽  
Bridge Piers ◽  
Machine Learning Techniques ◽  
Group Method ◽  
Scour Depth ◽  
Clear Water ◽  
Sediment Size ◽  
Learning Techniques ◽  
Scour Hazard ◽  
Bed Scour

Abstract Scour around bridge piers is a complex phenomenon and it is essential to assess or predict the scour hazard around bridge piers in tandem with completely understanding its mechanism. To date, there is no exact method for the estimation of scour depth. Nowadays, machine learning techniques are being recognized as effective tools for the prediction of scour depth using experimental data. In the present study, gradient tree boosting (GTB) technique was used for the prediction of scour depth around various pier shapes under different streambed conditions. Sediment size, sediment quantity, velocity, and flow time were used as input parameters to predict the scour depth under clear-water and live-bed scour conditions. The scour depth was predicted for different pier shapes such as, circular, rectangular, round-nosed and sharp-nosed shaped. The GTB model predicted scour depth values were compared with that of the group method of data handling (GMDH) technique. The performance of GTB and GMDH models were then evaluated based on statistical indices such as RRMSE, NNSE, WI, MNE, SI, and KGE. The study concludes that the GTB model performance was relatively superior to that of GMDH in the prediction of scour depth around different pier shapes.

scholarly journals Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

2021 ◽  
Vol 13 (18) ◽  
pp. 3573
Author(s):  
Chunfang Kong ◽  
Yiping Tian ◽  
Xiaogang Ma ◽  
Zhengping Weng ◽  
Zhiting Zhang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Random Forest ◽  
Landslide Susceptibility ◽  
Roc Curves ◽  
Support Vector ◽  
Swarm Optimization ◽  
Svm Model ◽  
Vector Machines ◽  
Susceptibility Evaluation ◽  
Landslide Disaster

Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.

Clear-water local scour at wide piers in shallow-water flow

2014 ◽  
Vol 9 (3) ◽  
pp. 331-343 ◽  
Author(s):  
N. Ahmad ◽  
T. Mohamed ◽  
F. H. Ali ◽  
B. Yusuf
Keyword(s):  
Laboratory Data ◽  
Local Scour ◽  
The Other ◽  
Scour Depth ◽  
Temporal Development ◽  
Clear Water ◽  
Shallow Water Flow ◽  
Equilibrium Time ◽  
Sediment Size ◽  
Flow Intensity

Laboratory data for local scour depth regarding the size of wide piers are presented. Clear water scour tests were performed for various pier widths (0.06, 0.076, 0.102, 0.14 and 0.165 m), two types of pier shapes (circular and rectangular) and two types of uniform cohesionless bed sediment (d50 = 0.23 and d50 = 0.80 mm). New data are presented and used to demonstrate the effects of pier width, pier shape and sediment size on scour depth. The influence of equilibrium time (te) on scouring processes is also discussed. Equilibrium scour depths were found to decrease with increasing values of b/d50. The temporal development of equilibrium local scour depth with new laboratory data is demonstrated for flow intensity V/Vc = 0.95. On the other hand, the results of scour mechanism have shown a significant relationship between normalized volume of scoured and deposited with pier width, b. The experimental data obtained in this study and data available from the literature for wide piers are used to evaluate predictions of existing methods.

Water quality prediction using machine learning methods

2018 ◽  
Vol 53 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Amir Hamzeh Haghiabi ◽  
Ali Heidar Nasrolahi ◽  
Abbas Parsaie
Keyword(s):  
Water Quality ◽  
Kernel Functions ◽  
Support Vector ◽  
Group Method ◽  
Water Quality Prediction ◽  
Machine Learning Methods ◽  
Svm Model ◽  
Different Types ◽  
Artificial Neural Network Ann ◽  
Southwest Of Iran

Abstract This study investigates the performance of artificial intelligence techniques including artificial neural network (ANN), group method of data handling (GMDH) and support vector machine (SVM) for predicting water quality components of Tireh River located in the southwest of Iran. To develop the ANN and SVM, different types of transfer and kernel functions were tested, respectively. Reviewing the results of ANN and SVM indicated that both models have suitable performance for predicting water quality components. During the process of development of ANN and SVM, it was found that tansig and RBF as transfer and kernel functions have the best performance among the tested functions. Comparison of outcomes of GMDH model with other applied models shows that although this model has acceptable performance for predicting the components of water quality, its accuracy is slightly less than ANN and SVM. The evaluation of the accuracy of the applied models according to the error indexes declared that SVM was the most accurate model. Examining the results of the models showed that all of them had some over-estimation properties. By evaluating the results of the models based on the DDR index, it was found that the lowest DDR value was related to the performance of the SVM model.

scholarly journals Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yudong Li ◽  
Zhongke Feng ◽  
Shilin Chen ◽  
Ziyu Zhao ◽  
Fengge Wang
Keyword(s):  
Neural Network ◽  
Support Vector Machines ◽  
Forest Fire ◽  
Bp Neural Network ◽  
Forest Fires ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Autonomous Region ◽  
Svm Model ◽  
Vector Machines

The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

scholarly journals A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 89-103 ◽  
Author(s):  
Hossein Hassani ◽  
Emmanuel Silva ◽  
Marine Combe ◽  
Demetra Andreou ◽  
Mansi Ghodsi ◽  
...  
Keyword(s):  
Freshwater Fish ◽  
Support Vector ◽  
Fish Disease ◽  
Disease Emergence ◽  
Freshwater Biodiversity ◽  
Chi Square ◽  
Svm Model ◽  
Vector Machines ◽  
Accuracy Rates ◽  
High Fish

Disease emergence, in the last decades, has had increasingly disproportionate impacts on aquatic freshwater biodiversity. Here, we developed a new model based on Support Vector Machines (SVM) for predicting the risk of freshwater fish disease emergence in England. Following a rigorous training process and simulations, the proposed SVM model was validated and reported high accuracy rates for predicting the risk of freshwater fish disease emergence in England. Our findings suggest that the disease monitoring strategy employed in England could be successful at preventing disease emergence in certain parts of England, as areas in which there were high fish introductions were not correlated with high disease emergence (which was to be expected from the literature). We further tested our model’s predictions with actual disease emergence data using Chi-Square tests and test of Mutual Information. The results identified areas that require further attention and resource allocation to curb future freshwater disease emergence successfully.

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