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Ozgur Kisi - One of the best experts on this subject based on the ideXlab platform.
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rm5Tree radial basis m5 Model Tree for accurate structural reliability analysis
Reliability Engineering & System Safety, 2018Co-Authors: Behrooz Keshtegar, Ozgur KisiAbstract:Abstract The surrogate Models-based prediction of performance functions is an efficient and accurate methodology in structural reliability analyses. In this paper, the M5 Model Tree (M5Tree) is improved based on radial basis training data set and it is named as Radial basis M5Tree (RM5Tree). To predict the performance function, the random input variables are transferred from ordinal space to radial space using several effective points for nonlinear calibrated Model of RM5Tree. The input datasets are controlled using the radial dataset for high-dimensional reliability problems to reduce computational efforts to evaluate the performance function. The abilities of RM5Tree using Monte Carlo Simulation (MCS) with respect to accuracy and efficiency are investigated through five nonlinear reliability problems. The results indicate that the proposed RM5Tree performs superior manner in accuracy and efficiency compared to the M5Tree, response surface method (RSM) and first order reliability method.
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stream flow forecasting of poorly gauged mountainous watershed by least square support vector machine fuzzy genetic algorithm and m5 Model Tree using climatic data from nearby station
Water Resources Management, 2018Co-Authors: Rana Muhammad Adnan, Ozgur Kisi, Xiaohui Yuan, Muhammad Adnan, Asif MehmoodAbstract:Forecasting stream flow is a very importance issue in water resources planning and management. The ability of three soft computing methods, least square support vector machine (LSSVM), fuzzy genetic algorithm (FGA) and M5 Model Tree (M5T), in forecasting daily and monthly stream flows of poorly gauged mountainous watershed using nearby hydro-meteorological data is investigated in the current study. In the first application, monthly stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. LSSVM provides slightly better forecasts than the FGA and M5T Models. Stream flow and temperature inputs generally give better forecasts compared to other inputs. In the second application, daily stream flows of Hunza river are forecasted using local stream flow data of Hunza and precipitation and temperature data of nearby station. Better results are obtained from the Models comprising only stream flow inputs. In general, a better accuracy is obtained from LSSVM Models in relative to the FGA and M5T. The results indicate that the monthly and daily stream flows of Hunza can be accurately forecasted by using only nearby climatic data. In the third application, daily stream flows of Hunza river are forecasted using local stream flow and climatic data and the Models’ accuracy is slightly increased in relative to the previous applications. LSSVM generally performs superior to the FGA and M5T in forecasting daily stream flow of Hunza river using local stream flow and climatic inputs.
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Modelling daily dissolved oxygen concentration using least square support vector machine multivariate adaptive regression splines and m5 Model Tree
Journal of Hydrology, 2018Co-Authors: Salim Heddam, Ozgur KisiAbstract:Abstract In the present study, three types of artificial intelligence techniques, least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5T) are applied for Modeling daily dissolved oxygen (DO) concentration using several water quality variables as inputs. The DO concentration and water quality variables data from three stations operated by the United States Geological Survey (USGS) were used for developing the three Models. The water quality data selected consisted of daily measured of water temperature (TE, °C), pH (std. unit), specific conductance (SC, μS/cm) and discharge (DI cfs), are used as inputs to the LSSVM, MARS and M5T Models. The three Models were applied for each station separately and compared to each other. According to the results obtained, it was found that: (i) the DO concentration could be successfully estimated using the three Models and (ii) the best Model among all others differs from one station to another.
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comparison of four heuristic regression techniques in solar radiation Modeling kriging method vs rsm mars and m5 Model Tree
Renewable & Sustainable Energy Reviews, 2018Co-Authors: Behrooz Keshtegar, Cihan Mert, Ozgur KisiAbstract:Abstract In this study, four different heuristic regression methods including Kriging, response surface method (RSM), multivariate adaptive regression (MARS) and M5 Model Tree (M5Tree) have been investigated for accurate estimating of solar radiation with different input data. Monthly solar radiation (SR) from Adana and Antakya stations, which are located in Eastern Mediterranean Region of Turkey is estimated based on the input data of maximum temperature (T max ), minimum temperature (T min ), sunshine hours (H s ), wind speed (W s ), and relative humidity (RH). In Adana station, the best MARS Model provided slightly better accuracy than the Kriging, RSM and M5Tree while the Kriging was found to be the better than the MARS, RSM and M5Tree in Antakya station. The predictions of M5Tree Model are shown inaccurate results for both maximum errors and minimum agreement compared to another Models. The effect of periodicity input is examined to obtain the accurate predictions of solar radiation for these stations based on the four heuristic –based Modeling Kriging, MARS, RSM, M5Tree approaches. Periodicity input data improved the root mean square errors of the best MARS, RSM, M5Tree and Kriging Models as 34%, 37%, 46% and 39% for Adana station and by 51%, 47%, 38% and 49% for Antakya station, respectively. The periodic Kriging Models performed superior to the periodic MARS, RSM and M5Tree Models.
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m5 Model Tree and monte carlo simulation for efficient structural reliability analysis
Applied Mathematical Modelling, 2017Co-Authors: Behrooz Keshtegar, Ozgur KisiAbstract:Abstract Practically, the performance of many engineering problems can be defined using a complex implicit limit state function. Approximation of the accurate failure probability is very time-consuming and inefficient based on Monte Carlo simulation (MCS) for complex performance functions. M5 Model Tree (M5Tree) Model is robust approach for simulation and prediction phenomena, which provides ability to dealing with complex implicit problems by dividing them into smaller problems. By improving the efficiency of reliability method using accurate approximated failure probability, an efficient reliability method using the MCS and M5Tree is proposed to calibrate the performance function and estimate the failure probability, respectively. The superiorities including simplicity and accuracy of M5Tree meta-Model are investigated to evaluate the actual performance function through five nonlinear complex mathematical and structural reliability problems. The proposed reliability method-based MCS and M5Tree improved the computational efforts for evaluating the performance function in reliability analysis. The M5Tree significantly increased the efficiency of reliability analysis with accurate failure probability.
Ali Behnood - One of the best experts on this subject based on the ideXlab platform.
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prediction of the compressive strength of normal and high performance concretes using m5p Model Tree algorithm
Construction and Building Materials, 2017Co-Authors: Ali Behnood, Mahsa Modiri Gharehveran, Venous Behnood, Kursat Esat AlyamacAbstract:Abstract Compressive strength of concrete is one the parameters required in many design codes. A reliable prediction of it can save in time and cost by quickly generating the needed design data. In addition, it can reduce the material waste by reducing the number of trial mixes. In this study, M5P Model Tree algorithm was used to predict the compressive strength of normal concrete (NC) and high performance concrete (HPC). Compared to other soft computing methods, Model Trees are able to offer two main advantages: (a) they are able to provide mathematical equations and offer more insight into the obtained equations and (b) they are more convenient to develop and implement. To develop the Model Tree, a total of 1912 distinctive data records were collected from internationally published literature. Overall, the results show that M5P Model Tree can be a better alternative approach for prediction of the compressive strength of NC and HPC using the amount of constituents of concrete as input parameters.
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evaluation of the splitting tensile strength in plain and steel fiber reinforced concrete based on the compressive strength
Construction and Building Materials, 2015Co-Authors: Ali Behnood, Kho Pin Verian, Mahsa Modiri GharehveranAbstract:Abstract Compressive strength (fc) and splitting tensile strength (fspt) of concrete are two important parameters in structural design. Due to the complexity, cost, and time-consuming nature of performing tensile tests, many researchers are interested to predict the value of this property in a simplified but accurate manner. This paper presents non-linear regression (NLR) analysis, artificial neural network (ANN), support vector machine (SVM) and M5′ Model Tree (MT) techniques to predict the tensile strength (fspt) of concretes made with and without steel fiber reinforcement. Error measures were used to compare the performance of different Models including the Models developed in this study and those developed by other researchers. Results indicated that non-linear regression analysis, artificial neural network, support vector machine, and Model Tree algorithms can predict the splitting tensile strength of concretes made with and without steel fiber reinforcement with satisfactory accuracy. However, machine learning techniques such as ANN, M5′ Model Tree and SVM provided superior Models compared to NLR analysis.
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predicting modulus elasticity of recycled aggregate concrete using m5 Model Tree algorithm
Construction and Building Materials, 2015Co-Authors: Ali Behnood, Jan Olek, Michal A GlinickiAbstract:Abstract The use of recycled aggregates in concrete is on the rise, driven by economic and environmental concerns. However, most of the existing Models to predict the value of elastic modulus of concrete were developed for virgin aggregates and, as a result, they may often be inaccurate when applied to concrete made with recycled aggregate. In this study, the M5′ Model Tree algorithm was used to predict the elastic modulus of recycled aggregate concrete. The main advantages of the Model Tree algorithms are: (a) they output relatively simple mathematical Models (formulas) and (b) are more convenient to develop and employ compared with other soft computing methods. To develop the Model Tree presented in this paper, over 450 data records were collected from internationally published literature. Error measures were used to compare the performance of the M5′ algorithm output to the output from other existing Models. The results showed that the Model developed using the M5′ algorithm has accuracy over 80 percent, which is well above the accuracy the other Models.
Wei Chen - One of the best experts on this subject based on the ideXlab platform.
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shallow landslide susceptibility mapping a comparison between logistic Model Tree logistic regression naive bayes Tree artificial neural network and support vector machine algorithms
International Journal of Environmental Research and Public Health, 2020Co-Authors: Vietha Nhu, Nadhir Alansari, Wei Chen, Himan Shahabi, Ataollah Shirzadi, Sushant K Singh, John J Clague, Abolfazl Jaafari, Shaghayegh Miraki, Jie DouAbstract:Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for Modeling and validation processes. The performance of the Models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning Models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree Model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naive Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) Models. Therefore, we recommend the use of the Logistic Model Tree Model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
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spatial prediction of landslide susceptibility by combining evidential belief function logistic regression and logistic Model Tree
Geocarto International, 2019Co-Authors: Wei Chen, Xia Zhao, Himan Shahabi, Ataollah Shirzadi, Khabat Khosravi, Huichan Chai, Shuai Zhang, Lingyu Zhang, Yingtao ChenAbstract:AbstractIn this study, we introduced novel hybrid of evidence believe function (EBF) with logistic regression (EBF-LR) and logistic Model Tree (EBF-LMT) for landslide susceptibility Modelling. Four...
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spatial prediction of landslide susceptibility by combining evidential belief function logistic regression and logistic Model Tree
Geocarto International, 2019Co-Authors: Wei Chen, Xia Zhao, Himan Shahabi, Ataollah Shirzadi, Khabat Khosravi, Huichan Chai, Shuai Zhang, Lingyu Zhang, Yingtao Chen, Xiaojing WangAbstract:In this study, we introduced novel hybrid of evidence believe function (EBF) with logistic regression (EBF-LR) and logistic Model Tree (EBF-LMT) for landslide susceptibility Modelling. Fourteen conditioning factors were selected, including slope aspect, elevation, slope angle, profile curvature, plan curvature, topographic wetness index (TWI), stream sediment transport index (STI), stream power index (SPI), distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), and land use. The importance of factors was assessed using correlation attribute evaluation method. Finally, the performance of three Models was evaluated using the area under the curve (AUC). The validation process indicated that the EBF-LMT Model acquired the highest AUC for the training (84.7%) and validation (76.5%) datasets, followed by EBF-LR and EBF Models. Our result also confirmed that combination of a decision Tree-logistic regression-based algorithm with a bivariate statistical Model lead to enhance the prediction power of individual landslide Models.
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a novel ensemble approach of bivariate statistical based logistic Model Tree classifier for landslide susceptibility assessment
Geocarto International, 2018Co-Authors: Wei Chen, Himan Shahabi, Ataollah Shirzadi, Chen Guo, Haoyuan Hong, Di Pan, Jiarui Hui, Baharin Bin AhmadAbstract:AbstractThis study addresses landslide susceptibility mapping (LSM) using a novel ensemble approach of using a bivariate statistical method (weights of evidence [WoE] and evidential belief function [EBF])-based logistic Model Tree (LMT) classifier. The performance and prediction capability of the ensemble Models were assessed using the area under the ROC curve (AUROC), standard error, 95% confidence intervals and significance level P. Model performance analyses indicated that the AUROC values of the WoE–LMT ensemble Model using the training and validation data-sets were 86.02 and 85.9%, respectively, whereas those of the EBF–LMT ensemble Model were 88.2 and 87.8%, respectively. On the other hand, the AUC curves for the four landslide susceptibility maps indicated that the AUC values of the ensemble Models of WoE–LMT (85.11 and 83.98%) and EBF–LMT (86.21 and 85.23%) could improve the performance and prediction accuracy of single WoE (84.23 and 82.46%) and EBF (85.39 and 81.33%) Models for the training and ...
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a comparative study of logistic Model Tree random forest and classification and regression Tree Models for spatial prediction of landslide susceptibility
Catena, 2017Co-Authors: Wei Chen, Haoyuan Hong, Jiale Wang, Biswajeet Pradhan, Zhao DuanAbstract:Abstract The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic Model Tree (LMT), random forest (RF), and classification and regression Tree (CART) Models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART Models were constructed using training data. Finally, the applied Models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three Models exhibit reasonably good performances; the RF Model exhibits the highest predictive capability compared with the LMT and CART Models. The RF Model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three Models are useful tools for spatial prediction of landslide susceptibility.
Nadhir Alansari - One of the best experts on this subject based on the ideXlab platform.
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shallow landslide susceptibility mapping a comparison between logistic Model Tree logistic regression naive bayes Tree artificial neural network and support vector machine algorithms
International Journal of Environmental Research and Public Health, 2020Co-Authors: Vietha Nhu, Nadhir Alansari, Wei Chen, Himan Shahabi, Ataollah Shirzadi, Sushant K Singh, John J Clague, Abolfazl Jaafari, Shaghayegh Miraki, Jie DouAbstract:Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for Modeling and validation processes. The performance of the Models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning Models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree Model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naive Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) Models. Therefore, we recommend the use of the Logistic Model Tree Model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.
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a comparative study of kernel logistic regression radial basis function classifier multinomial naive bayes and logistic Model Tree for flash flood susceptibility mapping
Water, 2020Co-Authors: Binh Thai Pham, Tran Van Phong, Huu Duy Nguyen, Nadhir Alansari, Ata Amini, Tran Thi Tuyen, Hoang Phan Hai Yen, Indra Prakash, Dieu Tien BuiAbstract:Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naive Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the Model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of Models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the Models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT Model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.
Jie Dou - One of the best experts on this subject based on the ideXlab platform.
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shallow landslide susceptibility mapping a comparison between logistic Model Tree logistic regression naive bayes Tree artificial neural network and support vector machine algorithms
International Journal of Environmental Research and Public Health, 2020Co-Authors: Vietha Nhu, Nadhir Alansari, Wei Chen, Himan Shahabi, Ataollah Shirzadi, Sushant K Singh, John J Clague, Abolfazl Jaafari, Shaghayegh Miraki, Jie DouAbstract:Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for Modeling and validation processes. The performance of the Models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning Models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree Model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naive Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) Models. Therefore, we recommend the use of the Logistic Model Tree Model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.