Articles | Volume 18, issue 6
https://doi.org/10.5194/nhess-18-1599-2018
https://doi.org/10.5194/nhess-18-1599-2018
Research article
 | 
12 Jun 2018
Research article |  | 12 Jun 2018

Learning in an interactive simulation tool against landslide risks: the role of strength and availability of experiential feedback

Pratik Chaturvedi, Akshit Arora, and Varun Dutt

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (22 Dec 2017) by Stefano Luigi Gariano
AR by Pratik Chaturvedi on behalf of the Authors (25 Dec 2017)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (03 Jan 2018) by Stefano Luigi Gariano
RR by Anonymous Referee #1 (05 Jan 2018)
RR by Anonymous Referee #4 (31 Jan 2018)
ED: Reconsider after major revisions (further review by editor and referees) (08 Feb 2018) by Stefano Luigi Gariano
AR by Pratik Chaturvedi on behalf of the Authors (20 Mar 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (21 Mar 2018) by Stefano Luigi Gariano
RR by Anonymous Referee #1 (03 Apr 2018)
ED: Publish subject to minor revisions (review by editor) (10 Apr 2018) by Stefano Luigi Gariano
AR by Pratik Chaturvedi on behalf of the Authors (22 Apr 2018)  Author's response   Manuscript 
ED: Publish as is (30 Apr 2018) by Stefano Luigi Gariano
AR by Pratik Chaturvedi on behalf of the Authors (06 May 2018)
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Short summary
Landslides are causing serious problems worldwide. To improve the risk communication, a Web-based interactive landslide simulator is used in this study. The ILS tool is based on the assumption that landslides occur due to environmental factors (spatial geology and rainfall) as well as human factors. The ILS also provides feedback in terms of fatality, injury, and property damage due to landslides. Results of an experiment with ILS suggest improved performance with repeated and rich feedback.
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