Climate Change Data Portal
DOI | 10.3390/s19122769 |
Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal | |
Acharya, Tri Dev1,2,3; Subedi, Anoj4; Lee, Dong Ha2 | |
发表日期 | 2019 |
ISSN | 1424-8220 |
卷号 | 19期号:12 |
英文摘要 | With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal's rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance. |
WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
来源期刊 | SENSORS |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/98974 |
作者单位 | 1.Kangwon Natl Univ, Ind Technol Inst, Chunchon 24341, South Korea; 2.Kangwon Natl Univ, Dept Civil Engn, Chunchon 24341, South Korea; 3.Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, Beijing 102616, Peoples R China; 4.Tribhuvan Univ, Inst Forestry, Pokhara Campus, Pokhara 33700, Nepal |
推荐引用方式 GB/T 7714 | Acharya, Tri Dev,Subedi, Anoj,Lee, Dong Ha. Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal[J],2019,19(12). |
APA | Acharya, Tri Dev,Subedi, Anoj,&Lee, Dong Ha.(2019).Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal.SENSORS,19(12). |
MLA | Acharya, Tri Dev,et al."Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal".SENSORS 19.12(2019). |
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