{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:03:21Z","timestamp":1776128601272,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61862038"],"award-info":[{"award-number":["61862038"]}]},{"name":"National Nature Science Foundation of China","award":["GSSYLXM-04"],"award-info":[{"award-number":["GSSYLXM-04"]}]},{"name":"National Nature Science Foundation of China","award":["21CX6JA150"],"award-info":[{"award-number":["21CX6JA150"]}]},{"name":"Educational Department of Gansu Province","award":["61862038"],"award-info":[{"award-number":["61862038"]}]},{"name":"Educational Department of Gansu Province","award":["GSSYLXM-04"],"award-info":[{"award-number":["GSSYLXM-04"]}]},{"name":"Educational Department of Gansu Province","award":["21CX6JA150"],"award-info":[{"award-number":["21CX6JA150"]}]},{"name":"Gansu Province Science and Technology Program\u2014Innovation Fund for Small and Medium-sized Enterprises","award":["61862038"],"award-info":[{"award-number":["61862038"]}]},{"name":"Gansu Province Science and Technology Program\u2014Innovation Fund for Small and Medium-sized Enterprises","award":["GSSYLXM-04"],"award-info":[{"award-number":["GSSYLXM-04"]}]},{"name":"Gansu Province Science and Technology Program\u2014Innovation Fund for Small and Medium-sized Enterprises","award":["21CX6JA150"],"award-info":[{"award-number":["21CX6JA150"]}]},{"name":"Data Project of National Cryosphere Desert Data Center (NCDC)","award":["61862038"],"award-info":[{"award-number":["61862038"]}]},{"name":"Data Project of National Cryosphere Desert Data Center (NCDC)","award":["GSSYLXM-04"],"award-info":[{"award-number":["GSSYLXM-04"]}]},{"name":"Data Project of National Cryosphere Desert Data Center (NCDC)","award":["21CX6JA150"],"award-info":[{"award-number":["21CX6JA150"]}]},{"name":"Foundation of a Hundred Youth Talents Training Program of Lanzhou Jiaotong University","award":["61862038"],"award-info":[{"award-number":["61862038"]}]},{"name":"Foundation of a Hundred Youth Talents Training Program of Lanzhou Jiaotong University","award":["GSSYLXM-04"],"award-info":[{"award-number":["GSSYLXM-04"]}]},{"name":"Foundation of a Hundred Youth Talents Training Program of Lanzhou Jiaotong University","award":["21CX6JA150"],"award-info":[{"award-number":["21CX6JA150"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The widely distributed \u201cStep-type\u201d landslides in the Three Gorges Reservoir (TGR) area have caused serious casualties and heavy economic losses. The prediction research of landslide displacement will be beneficial to the establishment of local geological hazard early warning systems for the realization of scientific disaster prevention and mitigation. However, the number of observed data like landslide displacement, rainfall, and reservoir water level in this area is very small, which results in difficulties for the training of advanced deep learning model to obtain more accurate prediction results. To solve the above problems, a Two-stage Combined Deep Learning Dynamic Prediction Model (TC-DLDPM) for predicting the typical \u201cStep-type\u201d landslides in the TGR area under the condition of small samples is proposed. The establishment process of this method is as follows: (1) the Dynamic Time warping (DTW) method is used to enhance the small samples of cumulative displacement data obtained by the Global Positioning System (GPS); (2) A Difference Decomposition Method (DDM) based on sequence difference is proposed, which decomposes the cumulative displacement into trend displacement and periodic displacement, and then the cubic polynomial fitting method is used to predict the trend displacement; (3) the periodic displacement component is predicted by the proposed TC-DLDPM model combined with external environmental factors such as rainfall and reservoir water level. The TC-DLDPM model combines the advantages of Convolutional Neural Network (CNN), Attention mechanism, and Long Short-term Memory network (LSTM) to carry out two-stage learning and parameter transfer, which can effectively realize the construction of a deep learning model for high-precision under the condition of small samples. A variety of advanced prediction models are compared with the TC-DLDPM model, and it is verified that the proposed method can accurately predict landslide displacement, especially in the case of drastic changes in external factors. The TC-DLDPM model can capture the spatio-temporal characteristics and dynamic evolution characteristics of landslide displacement, reduce the complexity of the model, and the number of model training calculations. Therefore, it provides a better solution and exploration idea for the prediction of landslide displacement under the condition of small samples.<\/jats:p>","DOI":"10.3390\/rs14153732","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T02:12:39Z","timestamp":1659665559000},"page":"3732","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Landslide Displacement Prediction Based on a Two-Stage Combined Deep Learning Model under Small Sample Condition"],"prefix":"10.3390","volume":"14","author":[{"given":"Chunxiao","family":"Yu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2395-4133","authenticated-orcid":false,"given":"Jiuyuan","family":"Huo","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"},{"name":"National Cryosphere Desert Data Center (NCDC), Lanzhou 730070, China"},{"name":"Lanzhou Ruizhiyuan Information Technology Co., Ltd., Lanzhou 730070, China"}]},{"given":"Chaojie","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8905-9006","authenticated-orcid":false,"given":"Yaonan","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Cryosphere Desert Data Center (NCDC), Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s11069-020-04337-6","article-title":"A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide","volume":"105","author":"Zhang","year":"2020","journal-title":"Nat. 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