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

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          Abstract

          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.

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          Benefits of the free and open Landsat data policy

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            Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

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              Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

              In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies. © 2010 Wiley Periodicals, Inc. Develop Neurobiol 71: 71–82, 2011
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                20 June 2019
                June 2019
                : 19
                : 12
                : 2769
                Affiliations
                [1 ]Institute of Industrial Technology, Kangwon National University, Chuncheon 24341, Korea; tridevacharya@ 123456kangwon.ac.kr
                [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; anojsubedi99@ 123456gmail.com
                Author notes
                [* ]Correspondence: geodesy@ 123456kangwon.ac.kr ; Tel.: +82-33-250-6232
                [†]

                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.

                Author information
                https://orcid.org/0000-0003-0886-4201
                https://orcid.org/0000-0002-3585-9005
                https://orcid.org/0000-0002-6934-1247
                Article
                sensors-19-02769
                10.3390/s19122769
                6631528
                31226778
                7e056801-d8f8-4af5-a2bb-4328df613e47
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 31 March 2019
                : 17 June 2019
                Categories
                Article

                Biomedical engineering
                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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