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Project of Shaanxi Key Laboratory of Land Consolidation","award":["2019-ZD04"],"award-info":[{"award-number":["2019-ZD04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide susceptibility mapping (LSM) is an important element of landslide risk assessment, but the process often needs to span multiple platforms and the operation process is complex. This paper develops an efficient user-friendly toolbox including the whole process of LSM, known as the SVM-LSM toolbox. The toolbox realizes landslide susceptibility mapping based on a support vector machine (SVM), which can be integrated into the ArcGIS or ArcGIS Pro platform. The toolbox includes three sub-toolboxes, namely: (1) influence factor production, (2) factor selection and dataset production, and (3) model training and prediction. Influence factor production provides automatic calculation of DEM-related topographic factors, converts line vector data to continuous raster factors, and performs rainfall data processing. Factor selection uses the Pearson correlation coefficient (PCC) to calculate the correlations between factors, and the information gain ratio (IGR) to calculate the contributions of different factors to landslide occurrence. Dataset sample production includes the automatic generation of non-landslide data, data sample production and dataset split. The accuracy, precision, recall, F1 value, receiver operating characteristic (ROC) and area under curve (AUC) are used to evaluate the prediction ability of the model. In addition, two methods\u2014single processing and multiprocessing\u2014are used to generate LSM. The prediction efficiency of multiprocessing is much higher than that of the single process. In order to verify the performance and accuracy of the toolbox, Wuqi County, Yan\u2019an City, Shaanxi Province was selected as the test area to generate LSM. The results show that the AUC value of the model is 0.8107. At the same time, the multiprocessing prediction tool improves the efficiency of the susceptibility prediction process by about 60%. The experimental results confirm the accuracy and practicability of the proposed toolbox in LSM.<\/jats:p>","DOI":"10.3390\/rs14143408","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T01:53:22Z","timestamp":1658109202000},"page":"3408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2856-9859","authenticated-orcid":false,"given":"Wubiao","family":"Huang","sequence":"first","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingtao","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Western China\u2019s Mineral Resource and Geological Engineering, Ministry of Education, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-7449","authenticated-orcid":false,"given":"Zhenhong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Western China\u2019s Mineral Resource and Geological Engineering, Ministry of Education, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqi","family":"Zhuang","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Key Laboratory of Western China\u2019s Mineral Resource and Geological Engineering, Ministry of Education, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling\u2019en","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"},{"name":"Big Data Center for Geosciences and Satellites, Chang\u2019an University, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Koley, B., Nath, A., Saraswati, S., Chatterjee, U., Bandyopadhyay, K., Bhatta, B., and Ray, B.C. 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