scholarly journals Estimation of Container Traffic at Seaports by Using Several Soft Computing Methods: A Case of Turkish Seaports

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Ümit Gökkuş ◽  
Mehmet Sinan Yıldırım ◽  
Metin Mutlu Aydin
Keyword(s):  
Soft Computing ◽  
Performance Metrics ◽  
Extreme Values ◽  
Computing Methods ◽  
Least Square ◽  
Support Vector ◽  
Traffic Forecasting ◽  
Forecasting Models ◽  
Bee Colony ◽  
Ann Models

Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances of the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul, Izmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out by using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic and demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable compared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to 60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey.

scholarly journals Comparison of six different soft computing methods in modeling evaporation in different climates

2016 ◽  
Author(s):  
Lunche Wang ◽  
Ozgur Kisi ◽  
Mohammad Zounemat-Kermani ◽  
Yiqun Gan
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Least Square ◽  
Support Vector ◽  
Climatic Data ◽  
Regression Methods ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Fuzzy Genetic ◽  
Different Climates

Abstract. Evaporation plays important roles in regional water resources management,terrestrial ecological process and regional climate change. This study investigated the abilities of six different soft computing methods, Multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at eight stations in different climates, air temperature (Ta), solar radiation (Rg), sunshine hours (Hs), relative humidity (RH) and wind speed (Ws) during 1961–2000 are used for model development and validation. The first part of applications focused on testing and comparing the model accuracies using different local input combinations. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations, while GRNN model performed better in Tibetan Plateau. The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. Generalized models were also developed and tested with data of eight stations. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ, CQ and HK stations.

Prediction of The Uniaxial Compressive Strength of Rocks Materials

2018 ◽  
pp. 31-96 ◽  
Author(s):  
Nurcihan Ceryan ◽  
Nuray Korkmaz Can
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
P Wave ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Artifical Neural Network ◽  
P Wave Velocity ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Direct And Indirect Methods

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.

A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms

2020 ◽  
pp. 1851-1885
Author(s):  
Bilal Ervural ◽  
Beyzanur Cayir Ervural ◽  
Cengiz Kahraman
Keyword(s):  
Soft Computing ◽  
Bat Algorithm ◽  
Computing Methods ◽  
Analytical Models ◽  
Grey Wolf Optimizer ◽  
Particle Swarm Algorithm ◽  
Bee Colony ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Firefly Optimization

Soft Computing techniques are capable of identifying uncertainty in data, determining imprecision of knowledge, and analyzing ill-defined complex problems. The nature of real world problems is generally complex and their common characteristic is uncertainty owing to the multidimensional structure. Analytical models are insufficient in managing all complexity to satisfy the decision makers' expectations. Under this viewpoint, soft computing provides significant flexibility and solution advantages. In this chapter, firstly, the major soft computing methods are classified and summarized. Then a comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed under some determined subject headings (classification topics) in a detailed way. The survey findings are supported with charts, bar graphs and tables to be more understandable.

scholarly journals Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Changxing Zhu ◽  
Hongbo Zhao ◽  
Ming Zhao
Keyword(s):  
Analytical Solution ◽  
Artificial Bee Colony ◽  
Optimal Algorithm ◽  
Back Analysis ◽  
Least Square ◽  
Support Vector ◽  
Underground Engineering ◽  
Bee Colony ◽  
Geomechanical Parameters ◽  
The Relationship

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms

2017 ◽  
pp. 34-68
Author(s):  
Bilal Ervural ◽  
Beyzanur Cayir Ervural ◽  
Cengiz Kahraman
Keyword(s):  
Soft Computing ◽  
Bat Algorithm ◽  
Computing Methods ◽  
Analytical Models ◽  
Grey Wolf Optimizer ◽  
Particle Swarm Algorithm ◽  
Bee Colony ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Firefly Optimization

Soft Computing techniques are capable of identifying uncertainty in data, determining imprecision of knowledge, and analyzing ill-defined complex problems. The nature of real world problems is generally complex and their common characteristic is uncertainty owing to the multidimensional structure. Analytical models are insufficient in managing all complexity to satisfy the decision makers' expectations. Under this viewpoint, soft computing provides significant flexibility and solution advantages. In this chapter, firstly, the major soft computing methods are classified and summarized. Then a comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed under some determined subject headings (classification topics) in a detailed way. The survey findings are supported with charts, bar graphs and tables to be more understandable.

APPLICATION OF A HYBRID OF LEAST SQUARE SUPPORT VECTOR MACHINE AND ARTIFICIAL BEE COLONY FOR BUILDING LOAD FORECASTING

2016 ◽  
Vol 78 (6-2) ◽  
Author(s):  
Mohammad Azhar Mat Daut ◽  
Mohammad Yusri Hassan ◽  
Hayati Abdullah ◽  
Hasimah Abdul Rahman ◽  
Md Pauzi Abdullah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hybrid Method ◽  
Artificial Bee Colony ◽  
Load Forecasting ◽  
Least Square ◽  
Electricity Production ◽  
Support Vector ◽  
Bee Colony ◽  
Forecasting Method ◽  
Building Load

Accurate load forecasting is an important element for proper planning and management of electricity production. Although load forecasting has been an important area of research, methods for accurate load forecasting is still scarce in the literature. This paper presents a study on a hybrid load forecasting method that combines the Least Square Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) methods for building load forecasting. The performance of the LSSVM-ABC hybrid method was compared to the LSSVM method in building load forecasting problems and the results has shown that the hybrid method is able to substantially improve the load forecasting ability of the LSSVM method.

Forecasting of Underactuated Robotic Finger Contact Forces by Support Vector Regression Methodology

2016 ◽  
Vol 30 (07) ◽  
pp. 1659019 ◽  
Author(s):  
Srđjan Jović ◽  
Amir Seyed Danesh ◽  
Emran Younesi ◽  
Obrad Aničić ◽  
Dalibor Petković ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Soft Computing ◽  
Analytical Modeling ◽  
Robotic Manipulators ◽  
Computing Methods ◽  
Basis Functions ◽  
Contact Forces ◽  
Support Vector ◽  
Body Model ◽  
Soft Computing Methods

Robotic manipulators have very strong nonlinearities. Analytical modeling of the robotic gripper is very challenging task. Therefore in this paper soft computing methods is applied in order to estimate contact forces of the robotic finger. Support vector regression (SVR) with radial and polynomial basis functions and the soft computing methods were used. The primary purpose of this study are in clarification of kinetostatic examining of a new finger mechanism utilizing pseudo-unbending body model. The results show the better prediction accuracy with SVR methodology with radial basis function (RBF).

Lateral Displacement of Liquefaction Induced Ground Using Least Square Support Vector Machine

2011 ◽  
Vol 2 (2) ◽  
pp. 29-39 ◽  
Author(s):  
Sarat Kumar Das ◽  
Pijush Samui ◽  
Dookie Kim ◽  
N. Sivakugan ◽  
Rajanikanta Biswal
Keyword(s):  
Support Vector Machine ◽  
Model Equation ◽  
Lateral Displacement ◽  
Least Square ◽  
Disaster Mitigation ◽  
Support Vector ◽  
Model Parameters ◽  
Ann Models ◽  
Artificial Neural Network Ann

The determination of lateral displacement of liquefaction induced ground during an earthquake is an imperative task in disaster mitigation. This study investigates the possibility of using least square support vector machine (LSSVM) for the prediction of lateral displacement of liquefaction induced ground during an earthquake. The results have been compared with those obtained using artificial neural network (ANN) models and observed that LSSVM outperformed the ANN models. Model equation has been presented based on the model parameters, which can be used by the professionals. Sensitivity analysis has also been performed to determine the importance of each of the input parameters.

scholarly journals Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Jong Wan Hu
Keyword(s):  
Support Vector Machine ◽  
Soft Computing ◽  
Prediction Models ◽  
Base Isolation ◽  
Least Square ◽  
Support Vector ◽  
Isolation System ◽  
Inference System ◽  
Comparison Results ◽  
Soft Computing Techniques

Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design and management of structures, especially for the high-risk areas. In this study, novel applications of advanced soft computing techniques are utilized for predicting the behavior of centrically braced frame (CBF) buildings with lead-rubber bearing (LRB) isolation system under ground motion effects. These techniques include least square support vector machine (LSSVM), wavelet neural networks (WNN), and adaptive neurofuzzy inference system (ANFIS) along with wavelet denoising. The simulation of a 2D frame model and eight ground motions are considered in this study to evaluate the prediction models. The comparison results indicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.

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