Slope Stability Monitoring Using Novel Remote Sensing Based Fuzzy Logic

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ID: 109986
2019
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Abstract
By the assist of remotely sensed data, this study examines the viability of slope stability monitoring using two novel conventional models. The proposed models are considered to be the combination of neuro-fuzzy (NF) system along with invasive weed optimization (IWO) and elephant herding optimization (EHO) evolutionary techniques. Considering the conditioning factors of land use, lithology, soil type, rainfall, distance to the road, distance to the river, slope degree, elevation, slope aspect, profile curvature, plan curvature, stream power index (SPI), and topographic wetness index (TWI), it is aimed to achieve a reliable approximation of landslide occurrence likelihood for unseen environmental conditions. To this end, after training the proposed EHO-NF and IWO-NF ensembles using training landslide events, their generalization power is evaluated by receiving operating characteristic curves. The results demonstrated around 75% accuracy of prediction for both models; however, the IWO-NF achieved a better understanding of landslide distribution pattern. Due to the successful performance of the implemented models, they could be promising alternatives to mathematical and analytical approaches being used for discerning the relationship between the slope failure and environmental parameters.
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moayedi2019sensorsslope Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hossein Moayedi;Dieu Tien Bui;Loke Kok Foong;Moayedi, Hossein;Tien Bui, Dieu;Kok Foong, Loke;
Journal sensors
Year 2019
DOI
10.3390/s19214636
URL
https://www.mdpi.com/1424-8220/19/21/4636/htm
Keywords
remote sensing invasive weed optimization slope stability monitoring National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine dieu tien bui hossein moayedi pmid:31653112 pmc6864694 doi:10.3390/s19214636 loke kok foong

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