Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366086142> ?p ?o ?g. }
- W4366086142 endingPage "105364" @default.
- W4366086142 startingPage "105364" @default.
- W4366086142 abstract "For decades, the distinction between statistical models and machine learning ones has been clear. The former are optimized to produce interpretable results, whereas the latter seeks to maximize the predictive performance of the task at hand. This is valid for any scientific field and for any method belonging to the two categories mentioned above. When attempting to predict natural hazards, this difference has lead researchers to make drastic decisions on which aspect to prioritize, a difficult choice to make. In fact, one would always seek the highest performance because at higher performances correspond better decisions for disaster risk reduction. However, scientists also wish to understand the results, as a way to rely on the tool they developed. Today, very recent development in deep learning have brought forward a new generation of interpretable artificial intelligence, where the prediction power typical of machine learning tools is equipped with a level of explanatory power typical of statistical approaches. In this work, we attempt to demonstrate the capabilities of this new generation of explainable artificial intelligence (XAI). To do so, we take the landslide susceptibility context as reference. Specifically, we build an XAI trained to model landslides occurred in response to the Gorkha earthquake (April 25, 2015), providing an educational overview of the model design and its querying opportunities. The results show high performance, with an AUC score of 0.89, while the interpretability can be extended to the probabilistic result assigned to single mapping units." @default.
- W4366086142 created "2023-04-19" @default.
- W4366086142 creator A5033916904 @default.
- W4366086142 creator A5086534279 @default.
- W4366086142 date "2023-07-01" @default.
- W4366086142 modified "2023-09-30" @default.
- W4366086142 title "Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling" @default.
- W4366086142 cites W1680670471 @default.
- W4366086142 cites W1966811787 @default.
- W4366086142 cites W1973249074 @default.
- W4366086142 cites W1983513512 @default.
- W4366086142 cites W1985076527 @default.
- W4366086142 cites W1988650824 @default.
- W4366086142 cites W1994454004 @default.
- W4366086142 cites W2001293281 @default.
- W4366086142 cites W2004200173 @default.
- W4366086142 cites W2006333257 @default.
- W4366086142 cites W2007471000 @default.
- W4366086142 cites W2021765639 @default.
- W4366086142 cites W2022197807 @default.
- W4366086142 cites W2027387749 @default.
- W4366086142 cites W2030675529 @default.
- W4366086142 cites W2032307057 @default.
- W4366086142 cites W2040698615 @default.
- W4366086142 cites W2042229599 @default.
- W4366086142 cites W2042585650 @default.
- W4366086142 cites W2044579603 @default.
- W4366086142 cites W2063987149 @default.
- W4366086142 cites W2076063813 @default.
- W4366086142 cites W2080859850 @default.
- W4366086142 cites W2081620141 @default.
- W4366086142 cites W2082507487 @default.
- W4366086142 cites W2085991257 @default.
- W4366086142 cites W2086063614 @default.
- W4366086142 cites W2089865782 @default.
- W4366086142 cites W2105714409 @default.
- W4366086142 cites W2115172421 @default.
- W4366086142 cites W2117350110 @default.
- W4366086142 cites W2129888542 @default.
- W4366086142 cites W2153989276 @default.
- W4366086142 cites W2159398439 @default.
- W4366086142 cites W2222654876 @default.
- W4366086142 cites W2261645655 @default.
- W4366086142 cites W2463798017 @default.
- W4366086142 cites W2507432949 @default.
- W4366086142 cites W2511416858 @default.
- W4366086142 cites W2519939567 @default.
- W4366086142 cites W2528305538 @default.
- W4366086142 cites W2554357049 @default.
- W4366086142 cites W2588237346 @default.
- W4366086142 cites W2729033468 @default.
- W4366086142 cites W2751065229 @default.
- W4366086142 cites W2751528411 @default.
- W4366086142 cites W2756823405 @default.
- W4366086142 cites W2793831793 @default.
- W4366086142 cites W2806372340 @default.
- W4366086142 cites W2882999202 @default.
- W4366086142 cites W2883540819 @default.
- W4366086142 cites W2912893284 @default.
- W4366086142 cites W2963796288 @default.
- W4366086142 cites W2965098473 @default.
- W4366086142 cites W2972534151 @default.
- W4366086142 cites W2995523160 @default.
- W4366086142 cites W2999729702 @default.
- W4366086142 cites W3006583570 @default.
- W4366086142 cites W3009636339 @default.
- W4366086142 cites W3009839749 @default.
- W4366086142 cites W3034212091 @default.
- W4366086142 cites W3038494274 @default.
- W4366086142 cites W3047918780 @default.
- W4366086142 cites W3056808037 @default.
- W4366086142 cites W3084262500 @default.
- W4366086142 cites W3156995693 @default.
- W4366086142 cites W3166420679 @default.
- W4366086142 cites W3212570739 @default.
- W4366086142 cites W4200108305 @default.
- W4366086142 cites W4200238674 @default.
- W4366086142 cites W4205294557 @default.
- W4366086142 cites W4224280405 @default.
- W4366086142 cites W4282559859 @default.
- W4366086142 doi "https://doi.org/10.1016/j.cageo.2023.105364" @default.
- W4366086142 hasPublicationYear "2023" @default.
- W4366086142 type Work @default.
- W4366086142 citedByCount "7" @default.
- W4366086142 countsByYear W43660861422023 @default.
- W4366086142 crossrefType "journal-article" @default.
- W4366086142 hasAuthorship W4366086142A5033916904 @default.
- W4366086142 hasAuthorship W4366086142A5086534279 @default.
- W4366086142 hasBestOaLocation W43660861421 @default.
- W4366086142 hasConcept C108583219 @default.
- W4366086142 hasConcept C111472728 @default.
- W4366086142 hasConcept C119857082 @default.
- W4366086142 hasConcept C127313418 @default.
- W4366086142 hasConcept C127413603 @default.
- W4366086142 hasConcept C138885662 @default.
- W4366086142 hasConcept C151730666 @default.
- W4366086142 hasConcept C154945302 @default.
- W4366086142 hasConcept C186295008 @default.