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| Result Number | Material Type | Add to My Shelf Action | Record Details and Options |
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Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factorsJournal of Rock Mechanics and Geotechnical Engineering, 2023-05, Vol.15 (5), p.1127-1143 [Peer Reviewed Journal]ISSN: 1674-7755 ;DOI: 10.1016/j.jrmge.2022.07.009Digital Resources/Online E-Resources |
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A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility predictionLandslides, 2020-01, Vol.17 (1), p.217-229 [Peer Reviewed Journal]Springer-Verlag GmbH Germany, part of Springer Nature 2019 ;2019© Springer-Verlag GmbH Germany, part of Springer Nature 2019 ;ISSN: 1612-510X ;EISSN: 1612-5118 ;DOI: 10.1007/s10346-019-01274-9Full text available |
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Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron modelLandslides, 2020-12, Vol.17 (12), p.2919-2930 [Peer Reviewed Journal]Springer-Verlag GmbH Germany, part of Springer Nature 2020 ;Springer-Verlag GmbH Germany, part of Springer Nature 2020. ;ISSN: 1612-510X ;EISSN: 1612-5118 ;DOI: 10.1007/s10346-020-01473-9Full text available |
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Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural NetworkSensors (Basel, Switzerland), 2020-03, Vol.20 (6), p.1576 [Peer Reviewed Journal]2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 by the authors. 2020 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s20061576 ;PMID: 32178235Full text available |
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Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter methodJournal of Rock Mechanics and Geotechnical Engineering, 2024-01, Vol.16 (1), p.213-230 [Peer Reviewed Journal]ISSN: 1674-7755 ;DOI: 10.1016/j.jrmge.2023.11.001Digital Resources/Online E-Resources |
| 6 |
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A novel landslide susceptibility prediction framework based on contrastive lossGIScience and remote sensing, 2024-12, Vol.61 (1) [Peer Reviewed Journal]ISSN: 1548-1603 ;EISSN: 1943-7226 ;DOI: 10.1080/15481603.2024.2306740Full text available |
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Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS TechnologiesRemote sensing (Basel, Switzerland), 2022-09, Vol.14 (18), p.4436 [Peer Reviewed Journal]2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs14184436Full text available |
| 8 |
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A novel CGBoost deep learning algorithm for coseismic landslide susceptibility predictionDi xue qian yuan., 2024-03, Vol.15 (2), p.101770, Article 101770 [Peer Reviewed Journal]2023 China University of Geosciences (Beijing) and Peking University ;ISSN: 1674-9871 ;DOI: 10.1016/j.gsf.2023.101770Full text available |
| 9 |
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Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning ModelsISPRS international journal of geo-information, 2020-06, Vol.9 (6), p.377 [Peer Reviewed Journal]ISSN: 2220-9964 ;EISSN: 2220-9964 ;DOI: 10.3390/ijgi9060377Full text available |
| 10 |
Material Type: Article
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Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value ModelsApplied sciences, 2019-09, Vol.9 (18), p.3664 [Peer Reviewed Journal]2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app9183664Full text available |
| 11 |
Material Type: Article
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Global landslide susceptibility prediction based on the automated machine learning (AutoML) frameworkGeocarto international, 2023-12, Vol.38 (1) [Peer Reviewed Journal]2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2023 ;ISSN: 1010-6049 ;EISSN: 1752-0762 ;DOI: 10.1080/10106049.2023.2236576Full text available |
| 12 |
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Landslide Susceptibility Prediction Based on Frequency Ratio Method and C5.0 Decision Tree ModelFrontiers in earth science (Lausanne), 2022-05, Vol.10 [Peer Reviewed Journal]ISSN: 2296-6463 ;EISSN: 2296-6463 ;DOI: 10.3389/feart.2022.918386Full text available |
| 13 |
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Landslide Susceptibility Prediction Based on Positive Unlabeled Learning Coupled With Adaptive SamplingIEEE journal of selected topics in applied earth observations and remote sensing, 2021, Vol.14, p.11581-11592 [Peer Reviewed Journal]Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 ;ISSN: 1939-1404 ;EISSN: 2151-1535 ;DOI: 10.1109/JSTARS.2021.3125741 ;CODEN: IJSTHZFull text available |
| 14 |
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Optimizing the Sample Selection of Machine Learning Models for Landslide Susceptibility Prediction Using Information Value Models in the Dabie Mountain Area of Anhui, ChinaSustainability, 2023-01, Vol.15 (3), p.1971 [Peer Reviewed Journal]COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2071-1050 ;EISSN: 2071-1050 ;DOI: 10.3390/su15031971Full text available |
| 15 |
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The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer LearningRemote sensing (Basel, Switzerland), 2024-01, Vol.16 (2), p.347 [Peer Reviewed Journal]COPYRIGHT 2024 MDPI AG ;2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs16020347Full text available |
| 16 |
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A comparative study of different machine learning models for landslide susceptibility prediction: a case study of Kullu-to-Rohtang pass transport corridor, IndiaEnvironmental earth sciences, 2023-04, Vol.82 (7), p.167, Article 167 [Peer Reviewed Journal]The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 1866-6280 ;EISSN: 1866-6299 ;DOI: 10.1007/s12665-023-10846-xFull text available |
| 17 |
Material Type: Article
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Uncertainties of landslide susceptibility prediction: influences of different study area scales and mapping unit scalesInternational journal of coal science & technology, 2024-12, Vol.11 (1), p.26-30 [Peer Reviewed Journal]The Author(s) 2024 ;COPYRIGHT 2024 Springer ;The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2095-8293 ;EISSN: 2198-7823 ;DOI: 10.1007/s40789-024-00678-wFull text available |
| 18 |
Material Type: Article
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Evaluation and Prediction of Landslide Susceptibility in Yichang Section of Yangtze River Basin Based on Integrated Deep Learning AlgorithmRemote sensing (Basel, Switzerland), 2022-06, Vol.14 (11), p.2717 [Peer Reviewed Journal]2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs14112717Full text available |
| 19 |
Material Type: Article
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Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point SelectionISPRS international journal of geo-information, 2022-07, Vol.11 (7), p.398 [Peer Reviewed Journal]2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2220-9964 ;EISSN: 2220-9964 ;DOI: 10.3390/ijgi11070398Full text available |
| 20 |
Material Type: Article
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Landslide susceptibility prediction using C5.0 decision tree modelE3S Web of Conferences, 2022-01, Vol.358, p.1015 [Peer Reviewed Journal]2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2267-1242 ;ISSN: 2555-0403 ;EISSN: 2267-1242 ;DOI: 10.1051/e3sconf/202235801015Full text available |
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