{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:03:22Z","timestamp":1774551802608,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41461089,41664002"],"award-info":[{"award-number":["41461089,41664002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Natural Science Foundation of China","award":["2015GXNSFAA139230"],"award-info":[{"award-number":["2015GXNSFAA139230"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD), approximate entropy and the weighted least square support vector machine (WLS-SVM) was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval.<\/jats:p>","DOI":"10.3390\/ijgi6010005","type":"journal-article","created":{"date-parts":[[2017,1,2]],"date-time":"2017-01-02T09:54:18Z","timestamp":1483350858000},"page":"5","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8199-3678","authenticated-orcid":false,"given":"Shaofeng","family":"Xie","sequence":"first","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueji","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongtian","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haifeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8026","DOI":"10.3390\/rs6098026","article-title":"Automated spatiotemporal landslide mapping over large areas using rapideye time series data","volume":"6","author":"Behling","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1007\/s11069-015-1799-2","article-title":"An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan","volume":"78","author":"Dou","year":"2015","journal-title":"Nat. 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