FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3145536616> ?p ?o ?g. }

• W3145536616 abstract "Landslide susceptibility mapping is very important for landslide risk evaluation and land use planning. Toward this end, this paper presents a case study in Ningqiang County, Shanxi Province, China. Slope units were selected as the basic mapping units. A traditional statistical certainty factor model (CF), a machine learning support vector machine model (SVM) and random forest model (RF), along with a hybrid CF-SVM model and a CF-RF model were applied to analyze landslide susceptibility. Firstly, 10 landslide conditioning factors were selected, namely slope-angle, altitude, slope aspect, degree of relief, lithology, distance to rivers, distance to faults, distance to roads, average annual rainfall and normalized difference vegetation index. The 23,169 slope units were generated from a Digital Elevation Model and the corresponding 10 conditioning factor layers were produced from both geological and geographical data. Then, landslide susceptibility mapping was carried out using the five models, respectively. Next, the landslide density (LD), frequency ratio (FR), the area under the curve (AUC) and other indicators were used to validate the rationality, performance and accuracy of the models. The results showed that the susceptibility maps produced from the different models were all reasonable. In each map, the LD and FR were greatest in the zones classed as having very high landslide susceptibility, followed by the high, moderate, low and very low landslide susceptibility classes, respectively. From the comparison of the different maps and ROC curves, the RF model based on slope units was the most appropriate for landslide susceptibility mapping in the study area. It was also found that the combination of weaker learner model (CF model here) with a stronger learner model (SVM and RF model here) can impact the applicability of the stronger model." @default.
• W3145536616 created "2021-04-13" @default.
• W3145536616 creator A5012283608 @default.
• W3145536616 creator A5030031724 @default.
• W3145536616 creator A5082958471 @default.
• W3145536616 date "2021-03-31" @default.
• W3145536616 modified "2023-10-16" @default.
• W3145536616 title "Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models" @default.
• W3145536616 cites W1483065104 @default.
• W3145536616 cites W188378287 @default.
• W3145536616 cites W1929661174 @default.
• W3145536616 cites W1969567670 @default.
• W3145536616 cites W1971988122 @default.
• W3145536616 cites W1980769777 @default.
• W3145536616 cites W1989158271 @default.
• W3145536616 cites W1993753658 @default.
• W3145536616 cites W2013177343 @default.
• W3145536616 cites W2018459837 @default.
• W3145536616 cites W2041859212 @default.
• W3145536616 cites W2044579603 @default.
• W3145536616 cites W2046812709 @default.
• W3145536616 cites W2062773906 @default.
• W3145536616 cites W2078971766 @default.
• W3145536616 cites W2129320258 @default.
• W3145536616 cites W2137034166 @default.
• W3145536616 cites W2147555471 @default.
• W3145536616 cites W2284304237 @default.
• W3145536616 cites W2333469642 @default.
• W3145536616 cites W2592479876 @default.
• W3145536616 cites W2610305023 @default.
• W3145536616 cites W2762568546 @default.
• W3145536616 cites W2767301512 @default.
• W3145536616 cites W2788433893 @default.
• W3145536616 cites W2795429897 @default.
• W3145536616 cites W2800958827 @default.
• W3145536616 cites W2805755209 @default.
• W3145536616 cites W2890865225 @default.
• W3145536616 cites W2891027816 @default.
• W3145536616 cites W2911424673 @default.
• W3145536616 cites W2911964244 @default.
• W3145536616 cites W2941679512 @default.
• W3145536616 cites W2981581709 @default.
• W3145536616 cites W2995502771 @default.
• W3145536616 cites W2998709485 @default.
• W3145536616 cites W3005068726 @default.
• W3145536616 cites W3009636339 @default.
• W3145536616 cites W3016783535 @default.
• W3145536616 cites W3027155096 @default.
• W3145536616 cites W3032213921 @default.
• W3145536616 cites W3032913569 @default.
• W3145536616 cites W3040332201 @default.
• W3145536616 cites W3049651666 @default.
• W3145536616 cites W4210949798 @default.
• W3145536616 cites W977406996 @default.
• W3145536616 doi "https://doi.org/10.3389/feart.2021.589630" @default.
• W3145536616 hasPublicationYear "2021" @default.
• W3145536616 type Work @default.
• W3145536616 sameAs 3145536616 @default.
• W3145536616 citedByCount "27" @default.
• W3145536616 countsByYear W31455366162021 @default.
• W3145536616 countsByYear W31455366162022 @default.
• W3145536616 countsByYear W31455366162023 @default.
• W3145536616 crossrefType "journal-article" @default.
• W3145536616 hasAuthorship W3145536616A5012283608 @default.
• W3145536616 hasAuthorship W3145536616A5030031724 @default.
• W3145536616 hasAuthorship W3145536616A5082958471 @default.
• W3145536616 hasBestOaLocation W31455366161 @default.
• W3145536616 hasConcept C114793014 @default.
• W3145536616 hasConcept C12267149 @default.
• W3145536616 hasConcept C122792734 @default.
• W3145536616 hasConcept C127313418 @default.
• W3145536616 hasConcept C142724271 @default.
• W3145536616 hasConcept C151730666 @default.
• W3145536616 hasConcept C154945302 @default.
• W3145536616 hasConcept C169258074 @default.
• W3145536616 hasConcept C181843262 @default.
• W3145536616 hasConcept C186295008 @default.
• W3145536616 hasConcept C205649164 @default.
• W3145536616 hasConcept C2524010 @default.
• W3145536616 hasConcept C2776133958 @default.
• W3145536616 hasConcept C33923547 @default.
• W3145536616 hasConcept C37054046 @default.
• W3145536616 hasConcept C41008148 @default.
• W3145536616 hasConcept C58640448 @default.
• W3145536616 hasConcept C62649853 @default.
• W3145536616 hasConcept C6350597 @default.
• W3145536616 hasConcept C71924100 @default.
• W3145536616 hasConceptScore W3145536616C114793014 @default.
• W3145536616 hasConceptScore W3145536616C12267149 @default.
• W3145536616 hasConceptScore W3145536616C122792734 @default.
• W3145536616 hasConceptScore W3145536616C127313418 @default.
• W3145536616 hasConceptScore W3145536616C142724271 @default.
• W3145536616 hasConceptScore W3145536616C151730666 @default.
• W3145536616 hasConceptScore W3145536616C154945302 @default.
• W3145536616 hasConceptScore W3145536616C169258074 @default.
• W3145536616 hasConceptScore W3145536616C181843262 @default.
• W3145536616 hasConceptScore W3145536616C186295008 @default.
• W3145536616 hasConceptScore W3145536616C205649164 @default.
• W3145536616 hasConceptScore W3145536616C2524010 @default.
• W3145536616 hasConceptScore W3145536616C2776133958 @default.

• next