An ensemble neural network approach for space-time landslide predictive modelling Article Swipe
Jana P. Lim
,
Giorgio Santinelli
,
Ashok Dahal
,
Anton Vrieling
,
Luigi Lombardo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.31223/x5198v
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.31223/x5198v
There is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on a single temporally-aggregated measure of rainfall derived from either in-situ measurements or satellite-based rainfall estimates. Relying on a summary metric of precipitation may not capture the complexity of the rainfall signal and its dynamics in space and time in triggering landslides. Here, we present a proof-of-concept for constructing a LEWS that is based on an integrated spatio-temporal modelling framework. Our proposed methodology builds upon a recent approach that uses a daily rainfall time series instead of the traditional cumulated scalar approximation. Specifically, we partition the study area into slope units and use a Gated Recurrent Unit (GRU) to process a satellite-derived rainfall time series and combine the output features with a second neural network (NN) tasked with capturing the effect of terrain characteristics. To assess if our approach enhances accuracy, we applied it in Vietnam and compared it against a standard modelling approach that incorporates terrain characteristics and cumulative rainfall over 14 days. Our protocol leads to better performance in hindcasting landslides when using past rainfall estimates (CHIRPS), as compared to the standard modelling approach. While not tested here, our approach can be extended to rainfall obtained from weather forecasts, potentially leading to actual landslide forecasts.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31223/x5198v
- https://eartharxiv.org/repository/object/6774/download/13033/
- OA Status
- gold
- Cited By
- 6
- References
- 111
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392191488
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4392191488Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31223/x5198vDigital Object Identifier
- Title
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An ensemble neural network approach for space-time landslide predictive modellingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-02-27Full publication date if available
- Authors
-
Jana P. Lim, Giorgio Santinelli, Ashok Dahal, Anton Vrieling, Luigi LombardoList of authors in order
- Landing page
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https://doi.org/10.31223/x5198vPublisher landing page
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https://eartharxiv.org/repository/object/6774/download/13033/Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://eartharxiv.org/repository/object/6774/download/13033/Direct OA link when available
- Concepts
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Landslide, Computer science, Terrain, Artificial neural network, Hindcast, Warning system, Partition (number theory), Environmental science, Meteorology, Data mining, Geology, Machine learning, Mathematics, Geography, Cartography, Telecommunications, Geotechnical engineering, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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111Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4385550052, https://openalex.org/W2082134041, https://openalex.org/W3009839749, https://openalex.org/W3120526585, https://openalex.org/W4205513197, https://openalex.org/W6683282530, https://openalex.org/W2230943196, https://openalex.org/W6674385629, https://openalex.org/W4313544709, https://openalex.org/W4385479464, https://openalex.org/W4281619551, https://openalex.org/W81280226, https://openalex.org/W3180748560, https://openalex.org/W106362265, https://openalex.org/W6821113053, https://openalex.org/W6757356151, https://openalex.org/W4388619298, https://openalex.org/W4299785280, https://openalex.org/W4309207080, https://openalex.org/W2075496252, https://openalex.org/W3009636339, https://openalex.org/W4386969082, https://openalex.org/W2261645655, https://openalex.org/W1980118872, https://openalex.org/W6725243535, https://openalex.org/W2769101041, https://openalex.org/W2990517150, https://openalex.org/W2147555471, https://openalex.org/W2058082754, https://openalex.org/W6790690058, https://openalex.org/W3081930112, https://openalex.org/W2133505387, https://openalex.org/W2616695016, https://openalex.org/W1836465849, https://openalex.org/W2005763135, https://openalex.org/W1575029535, https://openalex.org/W2003381260, https://openalex.org/W2005906706, https://openalex.org/W131069610, https://openalex.org/W2090105324, https://openalex.org/W2072049243, https://openalex.org/W4386089816, https://openalex.org/W2556522401, https://openalex.org/W4285736630, https://openalex.org/W2954332586, https://openalex.org/W3159928446, https://openalex.org/W6670392377, https://openalex.org/W4367307868, https://openalex.org/W4387008021, https://openalex.org/W4382809718, https://openalex.org/W6861564176, https://openalex.org/W2991227711, https://openalex.org/W4386171232, https://openalex.org/W2013713766, https://openalex.org/W1997757318, https://openalex.org/W2898170120, https://openalex.org/W3005575562, https://openalex.org/W6787779818, https://openalex.org/W2009610209, https://openalex.org/W2793831793, https://openalex.org/W2090097342, https://openalex.org/W2500686813, https://openalex.org/W2792175537, https://openalex.org/W2131241448, https://openalex.org/W2095705004, https://openalex.org/W3012313541, https://openalex.org/W6795388535, https://openalex.org/W1966760957, https://openalex.org/W2519939567, https://openalex.org/W4309180643, https://openalex.org/W6781448445, https://openalex.org/W2984550275, https://openalex.org/W2336324731, https://openalex.org/W2343905117, https://openalex.org/W3212570739, https://openalex.org/W6739901393, https://openalex.org/W3153851110, https://openalex.org/W2915483120, https://openalex.org/W4386694527, https://openalex.org/W2991191250, https://openalex.org/W2017298015, https://openalex.org/W6682799413, https://openalex.org/W4288438259, https://openalex.org/W2052531245, https://openalex.org/W2592104387, https://openalex.org/W3205608504, https://openalex.org/W4309710810, https://openalex.org/W2950260163, https://openalex.org/W2509507403, https://openalex.org/W2523246573, https://openalex.org/W4240501558, https://openalex.org/W3048470057, https://openalex.org/W4322005705, https://openalex.org/W2097998348, https://openalex.org/W4389513606, https://openalex.org/W1522301498, https://openalex.org/W4385245566, https://openalex.org/W3013341479, https://openalex.org/W3118822358, https://openalex.org/W4254238137, https://openalex.org/W3133696297, https://openalex.org/W2152575748, https://openalex.org/W4393161916, https://openalex.org/W3164108078, https://openalex.org/W2079958690, https://openalex.org/W3090309117, https://openalex.org/W4243587852, https://openalex.org/W3166420679, https://openalex.org/W4239510810, https://openalex.org/W4241010477, https://openalex.org/W4391443393 |
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