Prediction of landslide displacement based on GA-LSSVM with multiple factors

Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process

(2014) A. Garg et al.   ADVANCES IN ENGINEERING SOFTWARE

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

(2014) Maria De Giorgi et al.   Energies

Ash content prediction of coarse coal by image analysis and GA-SVM

(2014) Zelin Zhang et al.   POWDER TECHNOLOGY

Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet

(2014) A. Garg et al.   SIMULATION MODELLING PRACTICE AND THEORY

Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks

(2013) Luis Hernández et al.   Energies

Results uncertainty of support vector machine and hybrid of wavelet transform-support vector machine models for solid waste generation forecasting

(2013) M. Abbasi et al.   Environmental Progress & Sustainable Energy

An improvement in RBF learning algorithm based on PSO for real time applications

(2013) Vahid Fathi et al.   NEUROCOMPUTING

Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine

(2012) Cheng Lian et al.   NATURAL HAZARDS

Wind farm power prediction based on wavelet decomposition and chaotic time series

(2011) Xueli An et al.   EXPERT SYSTEMS WITH APPLICATIONS

Utilization of a least square support vector machine (LSSVM) for slope stability analysis

(2011) P. Samui et al.   Scientia Iranica

Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)

(2009) Daisaku Kawabata et al.   GEOMORPHOLOGY

Landslide prediction based on wavelet analysis and cusp catastrophe

(2009) Changdong Li et al.   Journal of Earth Science

Susceptibility analysis of shallow landslides source areas using physically based models

(2009) Giuseppe Sorbino et al.   NATURAL HAZARDS

An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps

(2008) H.A. Nefeslioglu et al.   ENGINEERING GEOLOGY

Artificial neural networks and cluster analysis in landslide susceptibility zonation

(2007) C. Melchiorre et al.   GEOMORPHOLOGY