Performance Evaluation and Comparison of Bivariate Statistical-Based Artificial Intelligence Algorithms for Spatial Prediction of Landslides

Bibliographic Data

ID22033835
AuthorsWei Chen (0000-0002-9419-148X, Xi'an University of Science and Technology, corresponding author), Zenghui Sun (0009-0005-6450-7905, Ministry of Natural Resources), Xia Zhao (0000-0003-2403-6924, Xi'an University of Science and Technology), Xinxiang Lei (0000-0001-7927-831X, Xi'an University of Science and Technology), Ataollah Shirzadi (0000-0003-1666-1180, University of Kurdistan), Himan Shahabi (0000-0001-5091-6947, University of Kurdistan)
Year2020
Volume9
Issue12
Pages696
Publication date2020-11-24
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi9120696
OpenAlexW3106670450
LanguageEN
References cited99

The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE–RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas

Algorithm · Bivariate analysis · Bivariate data · Cartography · Curvature · Geography · Geometry · Geomorphology · Landslide · Machine learning · Normalized Difference Vegetation Index · Receiver operating characteristic · Statistics · Support vector machine · Thematic map · Topographic Wetness Index · Computer Science · Geotechnical Engineering and Analysis · Landslides and related hazards · Mathematics · Tree Root and Stability Studies · Artificial Intelligence · Geology

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