FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4205157077> ?p ?o ?g. }

• W4205157077 endingPage "102681" @default.
• W4205157077 startingPage "102681" @default.
• W4205157077 abstract "Reliable landslide susceptibility mapping (LSM) is essential for disaster prevention and mitigation. This study develops a deep learning framework that integrates spatial response features and machine learning classifiers (SR-ML). The method has three steps. First, depthwise separable convolution (DSC) extracts spatial features to prevent confusion of multi-factor features. Second, spatial pyramid pooling (SPP) extracts response features to obtain features under different scales. Third, the high-level features are fused into prepared ML classifiers for more effective feature classification. This framework effectively extracts and uses different-dimension features of samples, explores ML classifiers for beneficial feature classification, and breaks through the limitation of fixed input sample sizes. In the Yarlung Zangbo Grand Canyon region, data on 203 landslides and 11 conditioning factors were prepared for availability verification and LSM. The evaluation indicated that the area under the receiver operating characteristic curve (AUC) for the proposed SR and SR-ML achieved 0.920 and 0.910, which were 6.6% and 5.6% higher than the random forest (RF, with the highest AUC in ML group) method, respectively. Furthermore, the framework using 64×64 size inputs had the lowest mean error of 0.01, revealing that samples considering landslide scales could improve performance for LSM." @default.
• W4205157077 created "2022-01-25" @default.
• W4205157077 creator A5007144110 @default.
• W4205157077 creator A5012044374 @default.
• W4205157077 creator A5014961194 @default.
• W4205157077 creator A5017764127 @default.
• W4205157077 creator A5031294692 @default.
• W4205157077 creator A5039142743 @default.
• W4205157077 date "2022-03-01" @default.
• W4205157077 modified "2023-10-14" @default.
• W4205157077 title "Combining spatial response features and machine learning classifiers for landslide susceptibility mapping" @default.
• W4205157077 cites W1536680647 @default.
• W4205157077 cites W1870144835 @default.
• W4205157077 cites W1902237438 @default.
• W4205157077 cites W1982623886 @default.
• W4205157077 cites W1988988192 @default.
• W4205157077 cites W1994214164 @default.
• W4205157077 cites W2002586590 @default.
• W4205157077 cites W2012118327 @default.
• W4205157077 cites W2057039778 @default.
• W4205157077 cites W2058082754 @default.
• W4205157077 cites W2060382151 @default.
• W4205157077 cites W2077337112 @default.
• W4205157077 cites W2081942226 @default.
• W4205157077 cites W2085507109 @default.
• W4205157077 cites W2089314377 @default.
• W4205157077 cites W2109255472 @default.
• W4205157077 cites W2143192068 @default.
• W4205157077 cites W2191761327 @default.
• W4205157077 cites W2205158676 @default.
• W4205157077 cites W2237443866 @default.
• W4205157077 cites W2500686813 @default.
• W4205157077 cites W2519746072 @default.
• W4205157077 cites W2522747841 @default.
• W4205157077 cites W2567326027 @default.
• W4205157077 cites W2606572359 @default.
• W4205157077 cites W2754252800 @default.
• W4205157077 cites W2763628386 @default.
• W4205157077 cites W2775745878 @default.
• W4205157077 cites W2789369267 @default.
• W4205157077 cites W2791665776 @default.
• W4205157077 cites W2804532080 @default.
• W4205157077 cites W2808860853 @default.
• W4205157077 cites W2895334315 @default.
• W4205157077 cites W2905019064 @default.
• W4205157077 cites W2908624219 @default.
• W4205157077 cites W2911893501 @default.
• W4205157077 cites W2912361013 @default.
• W4205157077 cites W2915483120 @default.
• W4205157077 cites W2916420886 @default.
• W4205157077 cites W2919115771 @default.
• W4205157077 cites W2921208133 @default.
• W4205157077 cites W2943844914 @default.
• W4205157077 cites W2965579150 @default.
• W4205157077 cites W2970904865 @default.
• W4205157077 cites W2972534151 @default.
• W4205157077 cites W2979806575 @default.
• W4205157077 cites W2980376317 @default.
• W4205157077 cites W2985753487 @default.
• W4205157077 cites W2990162685 @default.
• W4205157077 cites W2990262107 @default.
• W4205157077 cites W2990446629 @default.
• W4205157077 cites W2990986966 @default.
• W4205157077 cites W2993717253 @default.
• W4205157077 cites W2998212739 @default.
• W4205157077 cites W2999015335 @default.
• W4205157077 cites W3001147710 @default.
• W4205157077 cites W3003882269 @default.
• W4205157077 cites W3005376489 @default.
• W4205157077 cites W3009636339 @default.
• W4205157077 cites W3032913569 @default.
• W4205157077 cites W3037071415 @default.
• W4205157077 cites W3048285196 @default.
• W4205157077 cites W3049003094 @default.
• W4205157077 cites W3056808037 @default.
• W4205157077 cites W3080558256 @default.
• W4205157077 cites W3087103930 @default.
• W4205157077 cites W3130096399 @default.
• W4205157077 cites W3154456184 @default.
• W4205157077 cites W3165221400 @default.
• W4205157077 cites W3167998767 @default.
• W4205157077 cites W3179830881 @default.
• W4205157077 cites W3197879980 @default.
• W4205157077 cites W789984216 @default.
• W4205157077 cites W872839841 @default.
• W4205157077 doi "https://doi.org/10.1016/j.jag.2022.102681" @default.
• W4205157077 hasPublicationYear "2022" @default.
• W4205157077 type Work @default.
• W4205157077 citedByCount "12" @default.
• W4205157077 countsByYear W42051570772022 @default.
• W4205157077 countsByYear W42051570772023 @default.
• W4205157077 crossrefType "journal-article" @default.
• W4205157077 hasAuthorship W4205157077A5007144110 @default.
• W4205157077 hasAuthorship W4205157077A5012044374 @default.
• W4205157077 hasAuthorship W4205157077A5014961194 @default.
• W4205157077 hasAuthorship W4205157077A5017764127 @default.
• W4205157077 hasAuthorship W4205157077A5031294692 @default.
• W4205157077 hasAuthorship W4205157077A5039142743 @default.

• next