Slope stability is one of the most complex and essential issues for civil and geotechnical engineers, mainly due to life and high economical losses resulting from these failures. In this paper, a new approach is presented for estimating the Safety Factor (SF) for circular failure slope using hybrid support vector regression (SVR) and Ant Colony Optimization (ACO). The ACO is combined with the S...

Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed....

This study developed a novel model, HGA-SVR, for type of kernel function and kernel parameter value optimization in support vector regression (SVR), which is then applied to forecast the maximum electrical daily load. A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel function and kernel parameter values of SVR to increase the accuracy of SVR. The propos...

Due to the randomness and uncertainty in atmospheric environment, accompanied by a variety of unknown noise. Accurate prediction PM 2.5 concentration is very important for people prevent injury effectively. In order predict more accurately this hybrid modelling method support vector regression adaptive unscented Kalman filter (SVR-AUKF) proposed case incorrect or Firstly, model was established ...

Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they eliminate certain crucial questions involved by neural network construction. The main drawback of standard SVM is its high computational complexity, therefore recently a new technique, the Least Squares SVM (LS–SVM) has been introduced. In this paper we present an extended vie...

A potential solution to reduce high acquisition costs for airborne lidar (light detection and ranging) data is to combine lidar transects and optical satellite imagery to characterize forest vertical structure. Although multiple regression is typically used for such modeling, it seldom fully captures the complex relationships between forest variables. In an effort to improve these relationships...

The present work deals with the first application of Support Vector Regression (SVR) for the spatial data mapping. SVR is a recent development of the Statistical Learning Theory (VapnikChervonenkis theory). It is based on Structural Risk Minimisation and seems to be promising approach for the spatial data analysis and processing. There are several attractive properties of the SVR: robustness of...

This paper proposes a novel indoor Wireless Local Area Network (WLAN) positioning algorithm by using the Support Vector Machine (SVM) Hyperplane Margin Clustering and Regression (SVMCR). First of all, we rely on the SVM Hyperplane Margin Clustering (SVMC) to reduce the search space of the fingerprint database. Second, we use the Support Vector Regression (SVR) to characterize the relations of t...

Analyzing and forecasting the financial market based on the theory of phase space reconstruction of support vector regression. The key point of the phase space reconstruction is to choose the optimal delay time, and to find the optimal embedding dimension of space. This paper proposes the use of false nearest neighbor method to construct the error function for all the variables to determine the...

background: we aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (lr), decision tree (dt), artificial neural network (ann), and support vector machine (svm). methods: we used the dataset of a study conducted to predict risk factors of completed suicide in hamadan province, the west of iran, in 2010. to evaluate the high-risk grou...