{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:08:31Z","timestamp":1760148511100,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004739","name":"Lhasa Earth System Multi-Dimension Observatory Network, LEMON, Youth Innovation Promotion Association of the Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["Y2021044"],"award-info":[{"award-number":["Y2021044"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system.<\/jats:p>","DOI":"10.3390\/s23104714","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T10:49:51Z","timestamp":1683888591000},"page":"4714","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Monitoring Alarm Method of Dammed Lake Based on Hybrid Segmentation Algorithm"],"prefix":"10.3390","volume":"23","author":[{"given":"Ziming","family":"Cai","sequence":"first","affiliation":[{"name":"School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8957-3303","authenticated-orcid":false,"given":"Liang","family":"Sun","sequence":"additional","affiliation":[{"name":"Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"}]},{"given":"Baosheng","family":"An","sequence":"additional","affiliation":[{"name":"Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Xin","family":"Zhong","sequence":"additional","affiliation":[{"name":"Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"}]},{"given":"Wei","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Zhongyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Yan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Feng","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Resources, Environment and Materials, Guangxi University, Nanning 530004, China"}]},{"given":"Xinwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"ref_1","unstructured":"Skea, J., Shukla, P., and K\u0131lk\u0131\u015f, \u015e. 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