Application of GA–SVM method with parameter optimization for landslide development prediction

Abstract

Abstract. Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA-SVM) method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in a hydro-electrical engineering area of southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multifactor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multifactor SVM models of the landslide. The results show that the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models, and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest root mean square error (RMSE) of 0.0009 and the highest relation index (RI) of 0.9992.

Least Squares Support Vector Machine Model for Coordinate Transformation

“…These derived computational merits from LS-SVM can be confirmed in a number of studies found in the geodetic sciences domain (see e.g. Li & Kong, 2014;Mohammadi et al, 2015;Durmaz & Karslioglu, 2011;Ndehedehe, 2015, 2017 andreferences therein).…”

“…Artificial Neural Network (ANN) is one of the most attractive methods of computational intelligence to cope with non-linearity and time varying geodetic data due to its ability to learn and adapt to new dynamic environments. Numerous studies have shown successful implementation of ANN in the geodetic disciplines including but are not limited to deformation studies (Li & Kong, 2014;Huang, Wu, & Ziggah, 2016), meteorological studies (Mohammadi et al, 2015;Durmaz & Karslioglu, 2011), hydrological studies (Tiwari, J. Adamowski, & K. Adamowski, 2016;Deo & Şahin, 2016;Deo, Tiwari, Adamowski, & Quilty, 2017), tidal estimation (Okwuashi & Ndehedehe, 2017), change detection (Pal, 2009;Chang, Han, Yao, Chen, & Xu, 2010), geoid determination (Kavzoglu & Saka, 2005;Sorkhabi, 2015), and gravity field modelling (Turgut, 2016). Additionally, extensive studies on the suitability of ANN for coordinate transformation in both 2D and 3D have also been duly investigated by several authors (Tierra et al, 2008;Zaletnyik, 2004;Lin & Wang, 2006;Tierra, De Freitas, & Guevara, 2009;Tierra & Romero, 2014;Gullu, 2010;Gullu et al, 2011;Turgut, 2010;Mihalache, 2012;Yilmaz & Gullu, 2012;Konakoğlu, Cakir, & Gökalp, 2016;Ziggah, Youjian, Tierra, Konate, & Hui, 2016;Kumi-Boateng & Ziggah, 2017).…”

Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China

“…A search for optimal parameters in an SVM model plays a crucial role in building a landslide prediction model (Min and Lee 2005;Li and Kong 2014). To develop an efficient SVM model, C and r must be carefully predetermined.…”

Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation

“…For rainfall-induced landslides, atmospheric rainfall is one of the most susceptible disaster-causing factors and directly affects the periodic displacement of a landslide (Lian et al, 2015;Ren et al, 2015). So the periodic term can be regarded as a function of time and rainfall.…”