Identification of Characteristics of Land Cover in Mangkauk Catchment Area Using Support Vector Machine (SVM) And Artificial Neural Network (ANN)
Ichsan Ridwan1, Mohammad Bisri2, Fadly Hairannoor Yusran3, Luchman Hakim4 & Syarifuddin Kadir5 Show affiliations
- Department of Physiscs, Faculty of Mathematics and Natural Sciences, University of Lambung Mangkurat, South Kalimantan, Indonesia
- Department of Water Resources Engineering, Faculty of Engineering, University of Brawijaya, East Java, Indonesia
- Department of Soil Science, Faculty of Agriculture, University of Lambung Mangkurat South Kalimantan, Indonesia
- Department of Biology, Faculty of Mathematics and Natural Sciences, University of Brawijaya, East Java, Indonesia
- Department of Forestry, Faculty of Forestry, University of Lambung Mangkurat, South Kalimantan, Indonesia
American Journal of Applied Sciences, Volume 14, Issue 7, pp. 726–736 · doi.org/10.3844/ajassp.2017.726.736
Abstract
Land cover is anything that includes any types of appearance on the surface of the earth on a particular land. Information related to land cover can be used as at the parameter to determine the amount of runoff in a catchment area. This study was conducted in the Catchment Area (CA) of Mangkauk using Landsat 8 OLI/TIRS 2014 scene path/row 117/62 with the methods of Support Vector Machine (SVM) and Artificial Neural Network (ANN). The classification of land cover in Mangkauk catchment area included forests, plantations, shrubs, reeds/grasses, rice fields, open lands, settlements and water body. Based on the accuracy test of land cover classification using SVM, the value of the overall accuracy was 97.22% with Kappa Coefficient 0.96, while using ANN 86.33% with Kappa Coefficient 0.79.
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Ridwan, I., Bisri, M., Yusran, F. H., Hakim, L. & Kadir, S. (2017). Identification of Characteristics of Land Cover in Mangkauk Catchment Area Using Support Vector Machine (SVM) And Artificial Neural Network (ANN). American Journal of Applied Sciences, 14(7), 726–736. https://doi.org/10.3844/ajassp.2017.726.736
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