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Article

Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

1
Institute of Industrial Technology, Kangwon National University, Chuncheon 24341, Korea
2
Department of Civil Engineering, Kangwon National University, Chuncheon 24341, Korea
3
School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
4
Institute of Forestry, Pokhara Campus, Tribhuvan University, Pokhara 33700, Nepal
*
Author to whom correspondence should be addressed.
This paper is an extended version of the conference paper: Acharya, T.D.; Subedi, A.; Huang, H.; Lee, D.H. Classification of Surface Water using Machine Learning Methods from Landsat Data in Nepal. In Proceedings of the 5th International Electronic Conference on Sensors and Applications, 15–30 November 2018.
Received: 31 March 2019 / Revised: 12 June 2019 / Accepted: 17 June 2019 / Published: 20 June 2019
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. View Full-Text
Keywords: surface water mapping; machine learning; naive Bayes; recursive partitioning and regression trees; neural networks; support vector machines; random forest; gradient boosted machines; Landsat; Nepal surface water mapping; machine learning; naive Bayes; recursive partitioning and regression trees; neural networks; support vector machines; random forest; gradient boosted machines; Landsat; Nepal
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MDPI and ACS Style

Acharya, T.D.; Subedi, A.; Lee, D.H. Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal. Sensors 2019, 19, 2769. https://0-doi-org.brum.beds.ac.uk/10.3390/s19122769

AMA Style

Acharya TD, Subedi A, Lee DH. Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal. Sensors. 2019; 19(12):2769. https://0-doi-org.brum.beds.ac.uk/10.3390/s19122769

Chicago/Turabian Style

Acharya, Tri D., Anoj Subedi, and Dong H. Lee 2019. "Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal" Sensors 19, no. 12: 2769. https://0-doi-org.brum.beds.ac.uk/10.3390/s19122769

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