GIS-based rainfall-induced landslide susceptibility mapping: a comparative analysis of machine learning algorithms and a numerical method in Kvam, Norway Article Swipe
Haoyu Luo
,
Zhongqiang Liu
,
Yutao Pan
,
Irene Rocchi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu23-17051
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-egu23-17051
Analysis and prediction of climate-driven geohazards, such as rainfall-induced landslides and slope failures, are becoming more challenging given the changing climate where extreme events are inevitable. Therefore, there is a need to move beyond conventional sources of data and consider multiple types of data for more accurate analysis and prediction of landslides. In recent years, Data Fusion and Machine Learning techniques have played an important role in paving the path towards a better understanding of the problem and finding more accurate models at regional and local levels that incorporate several contributing factors for slope failures. The purpose of the study is thus to evaluate the capacities of machine learning models in landslide susceptibility prediction and analyze their model performance in comparison of a numerical method, Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS). Classic machine learning models, namely Multi-Layer Perceptron Neural Network (MLP), Random Forest (RF), Gradient Boosted Regression Tree (GBRT) and Extreme Gradient Boosting (XGBoost) are selected and developed respectively. The study is carried out based on a preliminary field survey of rainfall-induced landslides near Kvam village, Norway, in June 2011. A methodology workflow of landslide susceptibility modeling is proposed, in which effective data processing approaches including feature selection, data resampling, data splitting, and feature scaling are discussed and summarized. The optimal hyperparameter optimization method is determined by performing a comparative time efficiency analysis of Bayesian and Grid Search methods. It is concluded that GBRT is the optimal method for landslide susceptibility mapping in the study case of Kvam based on seven popular model evaluation metrics. Other tree-based machine learning algorithms (RF and XGBoost) also show an overall outstanding performance and computational efficiency in comparison to MLP and TRIGRS models. The landslide susceptibility maps developed by prediction results from five models are also presented and statistically analyzed. Corresponding model performance ranks are found with results from model evaluation metrics.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-egu23-17051
- OA Status
- gold
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4322020051
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4322020051Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/egusphere-egu23-17051Digital Object Identifier
- Title
-
GIS-based rainfall-induced landslide susceptibility mapping: a comparative analysis of machine learning algorithms and a numerical method in Kvam, NorwayWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-02-26Full publication date if available
- Authors
-
Haoyu Luo, Zhongqiang Liu, Yutao Pan, Irene RocchiList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-egu23-17051Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/egusphere-egu23-17051Direct OA link when available
- Concepts
-
Landslide, Computer science, Random forest, Machine learning, Artificial neural network, Hyperparameter optimization, Data mining, Grid, Algorithm, Perceptron, Artificial intelligence, Support vector machine, Geology, Geomorphology, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.levels | 86 |
| abstract_inverted_index.method | 216, 240 |
| abstract_inverted_index.models | 81, 109, 292 |
| abstract_inverted_index.namely | 138 |
| abstract_inverted_index.paving | 67 |
| abstract_inverted_index.played | 62 |
| abstract_inverted_index.recent | 53 |
| abstract_inverted_index.survey | 172 |
| abstract_inverted_index.years, | 54 |
| abstract_inverted_index.Boosted | 148 |
| abstract_inverted_index.Classic | 134 |
| abstract_inverted_index.Extreme | 153 |
| abstract_inverted_index.Machine | 58 |
| abstract_inverted_index.Network | 142 |
| abstract_inverted_index.Norway, | 179 |
| abstract_inverted_index.analyze | 115 |
| abstract_inverted_index.carried | 165 |
| abstract_inverted_index.climate | 20 |
| abstract_inverted_index.extreme | 22 |
| abstract_inverted_index.factors | 91 |
| abstract_inverted_index.feature | 199, 206 |
| abstract_inverted_index.finding | 78 |
| abstract_inverted_index.machine | 107, 135, 260 |
| abstract_inverted_index.mapping | 244 |
| abstract_inverted_index.method, | 124 |
| abstract_inverted_index.models, | 137 |
| abstract_inverted_index.models. | 281 |
| abstract_inverted_index.optimal | 213, 239 |
| abstract_inverted_index.overall | 269 |
| abstract_inverted_index.popular | 254 |
| abstract_inverted_index.problem | 76 |
| abstract_inverted_index.purpose | 96 |
| abstract_inverted_index.results | 289, 306 |
| abstract_inverted_index.scaling | 207 |
| abstract_inverted_index.several | 89 |
| abstract_inverted_index.sources | 35 |
| abstract_inverted_index.towards | 70 |
| abstract_inverted_index.Analysis | 0 |
| abstract_inverted_index.Bayesian | 227 |
| abstract_inverted_index.Boosting | 155 |
| abstract_inverted_index.Gradient | 147, 154 |
| abstract_inverted_index.Learning | 59 |
| abstract_inverted_index.Rainfall | 126 |
| abstract_inverted_index.Regional | 130 |
| abstract_inverted_index.XGBoost) | 265 |
| abstract_inverted_index.accurate | 46, 80 |
| abstract_inverted_index.analysis | 47, 225 |
| abstract_inverted_index.becoming | 14 |
| abstract_inverted_index.changing | 19 |
| abstract_inverted_index.consider | 39 |
| abstract_inverted_index.evaluate | 103 |
| abstract_inverted_index.learning | 108, 136, 261 |
| abstract_inverted_index.methods. | 231 |
| abstract_inverted_index.metrics. | 257, 310 |
| abstract_inverted_index.modeling | 189 |
| abstract_inverted_index.multiple | 40 |
| abstract_inverted_index.regional | 83 |
| abstract_inverted_index.selected | 158 |
| abstract_inverted_index.village, | 178 |
| abstract_inverted_index.workflow | 185 |
| abstract_inverted_index.(TRIGRS). | 133 |
| abstract_inverted_index.(XGBoost) | 156 |
| abstract_inverted_index.Transient | 125 |
| abstract_inverted_index.analyzed. | 298 |
| abstract_inverted_index.concluded | 234 |
| abstract_inverted_index.developed | 160, 286 |
| abstract_inverted_index.discussed | 209 |
| abstract_inverted_index.effective | 194 |
| abstract_inverted_index.failures, | 12 |
| abstract_inverted_index.failures. | 94 |
| abstract_inverted_index.important | 64 |
| abstract_inverted_index.including | 198 |
| abstract_inverted_index.landslide | 111, 187, 242, 283 |
| abstract_inverted_index.numerical | 123 |
| abstract_inverted_index.presented | 295 |
| abstract_inverted_index.proposed, | 191 |
| abstract_inverted_index.Grid-Based | 129 |
| abstract_inverted_index.Perceptron | 140 |
| abstract_inverted_index.Regression | 149 |
| abstract_inverted_index.Therefore, | 26 |
| abstract_inverted_index.algorithms | 262 |
| abstract_inverted_index.approaches | 197 |
| abstract_inverted_index.capacities | 105 |
| abstract_inverted_index.comparison | 120, 276 |
| abstract_inverted_index.determined | 218 |
| abstract_inverted_index.efficiency | 224, 274 |
| abstract_inverted_index.evaluation | 256, 309 |
| abstract_inverted_index.landslides | 9, 175 |
| abstract_inverted_index.performing | 220 |
| abstract_inverted_index.prediction | 2, 49, 113, 288 |
| abstract_inverted_index.processing | 196 |
| abstract_inverted_index.selection, | 200 |
| abstract_inverted_index.splitting, | 204 |
| abstract_inverted_index.techniques | 60 |
| abstract_inverted_index.tree-based | 259 |
| abstract_inverted_index.Multi-Layer | 139 |
| abstract_inverted_index.challenging | 16 |
| abstract_inverted_index.comparative | 222 |
| abstract_inverted_index.geohazards, | 5 |
| abstract_inverted_index.incorporate | 88 |
| abstract_inverted_index.inevitable. | 25 |
| abstract_inverted_index.landslides. | 51 |
| abstract_inverted_index.methodology | 184 |
| abstract_inverted_index.outstanding | 270 |
| abstract_inverted_index.performance | 118, 271, 301 |
| abstract_inverted_index.preliminary | 170 |
| abstract_inverted_index.resampling, | 202 |
| abstract_inverted_index.summarized. | 211 |
| abstract_inverted_index.Infiltration | 127 |
| abstract_inverted_index.contributing | 90 |
| abstract_inverted_index.conventional | 34 |
| abstract_inverted_index.optimization | 215 |
| abstract_inverted_index.Corresponding | 299 |
| abstract_inverted_index.computational | 273 |
| abstract_inverted_index.respectively. | 161 |
| abstract_inverted_index.statistically | 297 |
| abstract_inverted_index.understanding | 73 |
| abstract_inverted_index.climate-driven | 4 |
| abstract_inverted_index.hyperparameter | 214 |
| abstract_inverted_index.susceptibility | 112, 188, 243, 284 |
| abstract_inverted_index.Slope-Stability | 131 |
| abstract_inverted_index.rainfall-induced | 8, 174 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.7799999713897705 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.79065045 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |