Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling
Authors
Keywords
-
Journal
COMPUTERS & GEOSCIENCES
Volume 176, Issue -, Pages 105364
Publisher
Elsevier BV
Online
2023-04-17
DOI
10.1016/j.cageo.2023.105364
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Surface temperature controls the pattern of post-earthquake landslide activity
- (2022) Marco Loche et al. Scientific Reports
- Landslide Susceptibility Assessment by Using Convolutional Neural Network
- (2022) Shahrzad Nikoobakht et al. Applied Sciences-Basel
- Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides
- (2021) Hakan Tanyaş et al. Bulletin of Engineering Geology and the Environment
- The world's second-largest, recorded landslide event: Lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake
- (2021) Hakan Tanyaş et al. ENGINEERING GEOLOGY
- Deep learning-based landslide susceptibility mapping
- (2021) Mohammad Azarafza et al. Scientific Reports
- Parameter-free delineation of slope units and terrain subdivision of Italy
- (2020) Massimiliano Alviolicor et al. GEOMORPHOLOGY
- Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning
- (2020) Jie Dou et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
- (2020) Dieu Tien Bui et al. CATENA
- Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
- (2020) Zhice Fang et al. COMPUTERS & GEOSCIENCES
- Space-time landslide predictive modelling
- (2020) Luigi Lombardo et al. EARTH-SCIENCE REVIEWS
- Dynamic development of landslide susceptibility based on slope unit and deep neural networks
- (2020) Ye Hua et al. Landslides
- Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region
- (2020) Yaning Yi et al. CATENA
- Chrono-validation of near-real-time landslide susceptibility models via plug-in statistical simulations
- (2020) Luigi Lombardo et al. ENGINEERING GEOLOGY
- PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches
- (2019) Omid Rahmati et al. SCIENCE OF THE TOTAL ENVIRONMENT
- A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
- (2019) Faming Huang et al. Landslides
- Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment
- (2019) Gabriele Amato et al. ENGINEERING GEOLOGY
- XAI—Explainable artificial intelligence
- (2019) David Gunning et al. Science Robotics
- A review of statistically-based landslide susceptibility models
- (2018) Paola Reichenbach et al. EARTH-SCIENCE REVIEWS
- Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks
- (2018) Luigi Lombardo et al. GEODERMA
- The size, distribution, and mobility of landslides caused by the 2015 M w 7.8 Gorkha earthquake, Nepal
- (2018) Kevin Roback et al. GEOMORPHOLOGY
- Presenting logistic regression-based landslide susceptibility results
- (2018) Luigi Lombardo et al. ENGINEERING GEOLOGY
- A Global Empirical Model for Near-Real-Time Assessment of Seismically Induced Landslides
- (2018) M. A. Nowicki Jessee et al. JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE
- Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model
- (2017) Daniela Castro Camilo et al. ENVIRONMENTAL MODELLING & SOFTWARE
- Estimating the prediction performance of spatial models via spatial k-fold cross validation
- (2017) Jonne Pohjankukka et al. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
- Error bounds for approximations with deep ReLU networks
- (2017) Dmitry Yarotsky NEURAL NETWORKS
- Presentation and Analysis of a Worldwide Database of Earthquake-Induced Landslide Inventories
- (2017) Hakan Tanyaş et al. JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE
- Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north-eastern Sicily, Italy)
- (2016) Luigi Lombardo et al. EARTH SURFACE PROCESSES AND LANDFORMS
- Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy)
- (2016) L. Lombardo et al. NATURAL HAZARDS
- Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods
- (2016) Hamid Reza Pourghasemi et al. THEORETICAL AND APPLIED CLIMATOLOGY
- Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling
- (2015) J.N. Goetz et al. COMPUTERS & GEOSCIENCES
- A systematic review of landslide probability mapping using logistic regression
- (2015) M. E. A. Budimir et al. Landslides
- Deep learning in neural networks: An overview
- (2015) Jürgen Schmidhuber NEURAL NETWORKS
- The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes
- (2015) Chris Funk et al. Scientific Data
- Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets
- (2014) No-Wook Park Environmental Earth Sciences
- A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter
- (2013) Cory Merow et al. ECOGRAPHY
- Explaining prediction models and individual predictions with feature contributions
- (2013) Erik Štrumbelj et al. KNOWLEDGE AND INFORMATION SYSTEMS
- How can statistical models help to determine driving factors of landslides?
- (2012) Peter Vorpahl et al. ECOLOGICAL MODELLING
- Landslide Susceptibility Assessment and Validation in the Framework of Municipal Planning in Portugal: The Case of Loures Municipality
- (2012) Clemence Guillard et al. ENVIRONMENTAL MANAGEMENT
- Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models
- (2012) Iswar Das et al. GEOMORPHOLOGY
- Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy
- (2012) Cristiano Ballabio et al. Mathematical Geosciences
- Integrating physical and empirical landslide susceptibility models using generalized additive models
- (2011) Jason N. Goetz et al. GEOMORPHOLOGY
- Object-oriented mapping of landslides using Random Forests
- (2011) André Stumpf et al. REMOTE SENSING OF ENVIRONMENT
- Techniques for evaluating the performance of landslide susceptibility models
- (2009) Paolo Frattini et al. ENGINEERING GEOLOGY
- Different ways of landslide geometry interpretation in a process of statistical landslide susceptibility and hazard assessment: Horná Súča (western Slovakia) case study
- (2009) Martin Bednarik et al. Environmental Earth Sciences
- Statistical consensus methods for improving predictive geomorphology maps
- (2008) Mathieu Marmion et al. COMPUTERS & GEOSCIENCES
- Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning
- (2008) Robin Fell et al. ENGINEERING GEOLOGY
- Artificial neural networks and cluster analysis in landslide susceptibility zonation
- (2007) C. Melchiorre et al. GEOMORPHOLOGY
- Quantitative assessment of landslide susceptibility using high‐resolution remote sensing data and a generalized additive model
- (2007) N.‐W. Park et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started