scholarly journals Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis

2021 ◽  
Author(s):  
Mohammad Nobarinia ◽  
Farhoud Kalateh ◽  
Vahid Nourani ◽  
Alireza Babaeian Amini
Keyword(s):  
Uncertainty Analysis ◽  
Failure Time ◽  
Gene Expression Programming ◽  
Soil Characteristics ◽  
Dam Failure ◽  
Support Vector ◽  
Applied Model ◽  
Peak Outflow ◽  
Learning Machine ◽  
The Impact

Abstract Accurate prediction of a breached dam's peak outflow is a significant factor for flood risk analysis. In this study, the capability of Support Vector Machine and Kernel Extreme Learning Machine as kernel-based approaches and Gene Expression Programming method was assessed in breached dam peak outflow prediction. Two types of modeling were considered. First, only dam reservoir height and volume at the failure time were used as the input combinations (state 1). Then, soil characteristics were added to input combinations to investigate particularly the impact of soil characteristics (state 2). Results showed that the use of only soil characteristics did not lead to a desired accuracy; however, adding soil characteristics to input combinations (state 2) improved the models' accuracy up to 40%. The outcome of the applied models was also compared with existing empirical equations and it was found the applied models yielded better results. Sensitivity analysis results showed that dam height had the most important role in the peak outflow prediction, while the strength parameters did not have significant impacts. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was used and the results indicated that the SVM model had an allowable degree of uncertainty in peak outflow modelling.

scholarly journals Evaluation of the effective parameters on energy losses of rectangular and circular culverts via kernel-based approaches

2019 ◽  
Vol 21 (6) ◽  
pp. 1014-1029 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Ghazaleh Nasssaji Matin ◽  
Roghayeh Ghasempour ◽  
Seyed Mahdi Saghebian
Keyword(s):  
Uncertainty Analysis ◽  
Froude Number ◽  
Gene Expression Programming ◽  
Gaussian Process Regression ◽  
Performance Criteria ◽  
Bend Angle ◽  
Support Vector ◽  
Energy Losses ◽  
Effective Parameters ◽  
Model Efficiency

Abstract Energy dissipation in culverts is a complex phenomenon due to the nonlinearity and uncertainties of the process. In the current study, the capability of Gaussian process regression (GPR) and support vector machine (SVM) as kernel-based approaches and the gene expression programming (GEP) method was assessed in predicting energy losses in culverts. Two types of bend loss in rectangular culverts and entrance loss in circular culverts with different inlet end treatments were considered. Various input combinations were developed and tested using experimental data. The OAT (one-at-a-time), factorial sensitivity analysis and Monte Carlo uncertainty analysis were used to select the effective parameters in modeling. The results of performance criteria proved the capability of the applied methods (i.e. high correlation coefficient (R) and determination coefficient (DC) and low root mean square error (RSME)). For rectangular culverts, the model with parameters Fr (Froude number) and θ (bend angle), and for circular culverts, the model with parameters Fr and Hw/D (depth ratio), were the superior models. It showed that using the bend downstream Froude number caused an increment in model efficiency. Among the four end inlet treatments, mitered flush to 1.5:1 fill slope inlet yielded more accurate prediction. The sensitivity and uncertainty analysis showed that θ and Hw/D had the most significant impact on modeling, and Fr had the highest uncertainty.

scholarly journals Estimation of peak outflow in dam failure using neural network approach under uncertainty analysis

2015 ◽  
Vol 42 (5) ◽  
pp. 721-734 ◽  
Author(s):  
Farhad Hooshyaripor ◽  
Ahmad Tahershamsi ◽  
Kourosh Behzadian
Keyword(s):  
Neural Network ◽  
Uncertainty Analysis ◽  
Dam Failure ◽  
Network Approach ◽  
Neural Network Approach ◽  
Peak Outflow

scholarly journals Uncertainty assessment of kernel based approaches on scour depth modeling in downstream of ski-jump bucket spillways

2021 ◽  
Author(s):  
Redvan Ghasemlounia ◽  
Seyed Mahdi Saghebian
Keyword(s):  
Uncertainty Analysis ◽  
Uncertainty Assessment ◽  
The State ◽  
Point Of View ◽  
Accurate Estimation ◽  
Support Vector ◽  
Scour Depth ◽  
Process Comparison ◽  
Applied Model ◽  
Tail Water

Abstract From the hydraulic structures designer's point of view, the scour depth accurate estimation in downstream of spillways is so important. In this study, the scour depth was assessed downstream of ski-jump bucket spillways using two kernel based approaches namely Support Vector Machine (SVM) and Kernel Extreme Learning Machine (KELM). In the model developing process, two states were tested and the impacts of dimensional and non-dimensional parameters on model efficiency were assessed. The best applied model dependability was investigated via Monte Carlo uncertainty analysis. In addition, the model accuracy was compared with some available semi-theoretical formulas. It was observed that the applied models were more successful than available formulas. The sensitivity analysis results showed that q (unit discharge of spillway) variable in the state 1 and q2/[gYt3] (g is gravity acceleration and Yt is tail water depth) variable in the state 2 were the most significant parameters in the modeling process. Comparison among applied methods and one other intelligence approach showed that KELM was more successful in predicting process. The obtained results from uncertainty analysis indicated that the KELM model had an allowable degree of uncertainty in the scour depth modeling.

Possibilities to use the meta model and classical approaches to evaluate the impact of hydraulic conditions in prediction of the critical submergence depth ratio

2018 ◽  
Vol 19 (4) ◽  
pp. 1055-1065
Author(s):  
Ramin Vafaei Poursorkhabi ◽  
Roghayeh Ghasempour
Keyword(s):  
Gene Expression Programming ◽  
Water Withdrawal ◽  
Pipe Diameter ◽  
Support Vector ◽  
Meta Model ◽  
Intake Pipe ◽  
Depth Ratio ◽  
Critical Submergence ◽  
The Impact ◽  
Submergence Depth

Abstract One of the hydraulic phenomena that mainly occurs during the water withdrawal process of channels is the formation of vortices that can cause many problems for the hydro-mechanical facilities of intakes. In the current study, classical models and meta model approaches (i.e. Support Vector Machine and Gene Expression Programming) were applied to evaluate the impact of pipe diameter and hydraulic condition changes in prediction of the critical submergence depth ratio in horizontal intakes. In this regard, two types of critical submergence experiments, based on bottom clearance, were considered (i.e. c = 0 and c = d/2, in which c and d are the bottom clearance and diameter of the intake, respectively). Different models were developed and tested using experimental data series. The results indicated that in modeling the critical submergence depth ratio, meta model approaches led to better predictions compared to the classical approaches. It was observed that the developed models for the state of c = d/2 yielded better results. According to the outcome of sensitivity analysis, the ratio of velocities in the intake pipe and channel (Vi/Vc) had a key role in the modeling. It was also found that intake pipe diameter affected the critical submergence depth ratio in intake pipes. Increasing pipe diameter caused a decrease in model accuracy.

scholarly journals Evaluation of the effluent quality parameters of wastewater treatment plant based on uncertainty analysis and post-processing approaches (case study)

2021 ◽  
Vol 83 (7) ◽  
pp. 1633-1648
Author(s):  
Nasim Hejabi ◽  
Seyed Mahdi Saghebian ◽  
Mohammad Taghi Aalami ◽  
Vahid Nourani
Keyword(s):  
Wastewater Treatment ◽  
Uncertainty Analysis ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Ensemble Methods ◽  
Quality Parameters ◽  
Support Vector ◽  
Effluent Quality ◽  
Applied Model

Abstract Wastewater treatment plants (WWTPs) are highly complicated and dynamic systems and so their appropriate operation, control, and accurate simulation are essential. The simulation of WWTPs according to the process complexity has become an important issue in growing environmental awareness. In recent decades, artificial intelligence approaches have been used as effective tools in order to investigate environmental engineering issues. In this study, the effluent quality of Tabriz WWTP was assessed using two intelligence models, namely support Vector Machine (SVM) and artificial neural network (ANN). In this regard, several models were developed based on influent variables and tested via SVM and ANN methods. Three time scales, daily, weekly, and monthly, were investigated in the modeling process. On the other hand, since applied methods were sensitive to input variables, the Monte Carlo uncertainty analysis method was used to investigate the best-applied model dependability. It was found that both models had an acceptable degree of uncertainty in modeling the effluent quality of Tabriz WWTP. Next, ensemble approaches were applied to improve the prediction performance of Tabriz WWTP. The obtained results comparison showed that the ensemble methods represented better efficiency than single approaches in predicting the performance of Tabriz WWTP.

Intrusion Detection Using Deep Belief Network and Extreme Learning Machine

2015 ◽  
Vol 3 (2) ◽  
pp. 35-56 ◽  
Author(s):  
Zahangir Alom ◽  
Venkata Ramesh Bontupalli ◽  
Tarek M. Taha
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Training Data ◽  
Support Vector ◽  
Security Threats ◽  
Deep Belief Networks ◽  
Detection Systems ◽  
Testing Accuracy ◽  
Learning Machine ◽  
The Impact

Security threats for computer networks have increased dramatically over the last decade, becoming bolder and more brazen. There is a strong need for effective Intrusion Detection Systems (IDS) that are designed to interpret intrusion attempts in incoming network traffic intelligently. In this paper, the authors explored the capabilities of Deep Belief Networks (DBN) – one of the most influential deep learning approach – in performing intrusion detection after training with the NSL-KDD dataset. Additionally, they examined the impact of using Extreme Learning Machine (ELM) and Regularized ELM on the same dataset to evaluate the performance against DBN and Support Vector Machine (SVM) approaches. The trained system identifies any type of unknown attack in the dataset examined. In addition to detecting attacks, the proposed system also classifies them into five groups. The implementation with DBN and SVM give a testing accuracy of about 97.5% and 88.33% respectively with 40% of training data selected from the NSL-KDD dataset. On the other hand, the experimental results show around 98.20% and 98.26% testing accuracy respectively for ELM and RELM after reducing the data dimensions from 41 to 9 essential features with 40% training data. ELM and RELM perform better in terms of testing accuracy upon comparison with DBN and SVM.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

scholarly journals EPID-34. THE DETRIMENTAL EFFECT OF BIOPSY PRECEDING RESECTION IN SURGICALLY ACCESSIBLE GLIOBLASTOMAS: RESULTS FROM THE NATIONAL CANCER DATABASE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii85-ii86
Author(s):  
Ping Zhu ◽  
Xianglin Du ◽  
Angel Blanco ◽  
Leomar Y Ballester ◽  
Nitin Tandon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Failure Time ◽  
Binary Logistic Regression ◽  
Rank Test ◽  
Imaging Features ◽  
National Cancer Database ◽  
Prolonged Length ◽  
Logistic Regression Models ◽  
Operative Outcomes ◽  
The Impact

Abstract OBJECTIVES To investigate the impact of biopsy preceding resection compared to upfront resection in glioblastoma overall survival (OS) and post-operative outcomes using the National Cancer Database (NCDB). METHODS A total of 17,334 GBM patients diagnosed between 2010 and 2014 were derived from the NCDB. Patients were categorized into two groups: “upfront resection” versus “biopsy followed by resection”. Primary outcome was OS. Post-operative outcomes including 30-day readmission/mortality, 90-day mortality, and prolonged length of inpatient hospital stay (LOS) were secondary endpoints. Kaplan-Meier methods and accelerated failure time (AFT) models with gamma distribution were applied for survival analysis. Multivariable binary logistic regression models were performed to compare differences in the post-operative outcomes between these groups. RESULTS Patients undergoing “upfront resection” experienced superior survival compared to those undergoing “biopsy followed by resection” (median OS: 12.4 versus 11.1 months, log-rank test: P=0.001). In multivariable AFT models, significant survival benefits were observed among patients undergoing “upfront resection” (time ratio [TR]: 0.83, 95% CI: 0.75–0.93, P=0.001). Patients undergoing upfront GTR had the longest survival compared to upfront STR, GTR following STR, or GTR and STR following an initial biopsy (14.4 vs. 10.3, 13.5, 13.3, and 9.1, months), respectively (TR: 1.00 [Ref.], 0.75, 0.82, 0.88, and 0.67). Recent years of diagnosis, higher income and treatment at academic facilities were significantly associated with the likelihood of undergoing upfront resection after adjusting the covariates. Multivariable logistic regression revealed that 30-day mortality and 90-day mortality were decreased by 73% and 44% for patients undergoing “upfront resection” over “biopsy followed by resection”, respectively (both p < 0.001). CONCLUSIONS Pre-operative biopsies for surgically accessible tumors with characteristic imaging features of Glioblastoma lead to worse survival despite subsequent resection compared to patients undergoing upfront resection.

scholarly journals Prediction of Band Gap Energy of Doped Graphitic Carbon Nitride Using Genetic Algorithm-Based Support Vector Regression and Extreme Learning Machine

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 411
Author(s):  
Taoreed O. Owolabi ◽  
Mohd Amiruddin Abd Rahman
Keyword(s):  
Genetic Algorithm ◽  
Visible Light ◽  
Band Gap ◽  
Carbon Nitride ◽  
Activation Function ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Support Vector ◽  
Learning Machine ◽  
Photocatalytic Applications

Graphitic carbon nitride is a stable and distinct two dimensional carbon-based polymeric semiconductor with remarkable potentials in organic pollutants degradation, chemical sensors, the reduction of CO2, water splitting and other photocatalytic applications. Efficient utilization of this material is hampered by the nature of its band gap and the rapid recombination of electron-hole pairs. Heteroatom incorporation due to doping alters the symmetry of the semiconductor and has been among the adopted strategies to tailor the band gap for enhancing the visible-light harvesting capacity of the material. Electron modulation and enhancement of reaction active sites due to doping as evident from the change in specific surface area of doped graphitic carbon nitride is employed in this work for modeling the associated band gap using hybrid genetic algorithm-based support vector regression (GSVR) and extreme learning machine (ELM). The developed GSVR performs better than ELM-SINE (with sine activation function), ELM-TRANBAS (with triangular basis activation function) and ELM-SIG (with sigmoid activation function) model with performance enhancement of 69.92%, 73.59% and 73.67%, respectively, on the basis of root mean square error as a measure of performance. The four developed models are also compared using correlation coefficient and mean absolute error while the developed GSVR demonstrates a high degree of precision and robustness. The excellent generalization and predictive strength of the developed models would ultimately facilitate quick determination of the band gap of doped graphitic carbon nitride and enhance its visible-light harvesting capacity for various photocatalytic applications.

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