Using k Nearest Neighbor (k-NN) algorithm as a suitable approach to estimate cover-management factor of RUSLE model in Shirin Dareh basin, North Khorasan

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ZAKERI EMAD; KARIMZADEH HAMID REZA; MOUSAVI SEYED ALIREZA

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Journal Paper

Paper Information

Journal: Journal of Range and Watershed Managment Year:2021 | Volume:73 | Issue:4 Page(s): 753-770

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Title

Using k Nearest Neighbor (k-NN) algorithm as a suitable approach to estimate cover-management factor of RUSLE model in Shirin Dareh basin, North Khorasan

Author(s)

ZAKERI EMAD | KARIMZADEH HAMID REZA | MOUSAVI SEYED ALIREZA | Issue Writer Certificate

Keywords

Soil ErosionQ2

Cover-management factorQ1

Vegetation indicesQ2

GISQ1

nonparametric algorithmsQ1

k Nearest NeighborQ1

Abstract

Cover-management factor (C) is one of the most important influential factor on Soil Erosion using the Revised Universal Soil Loss Equation (RUSLE) model. C-factor is challenging to determine based on the proposed procedures due to lack of accurate information. Vegetation cover map can be used to estimate C-factor, but preparing a suitable mapping of vegetation cover is challenging in many situations. Therefore, in this study vegetation cover map was prepared and compared using the k Nearest Neighbor (k-NN) algorithm, linear regression (LR) and linear stepwise regression (LSR) in the study area. In regression methods, 17 vegetation and environmental indices were prepared and their relationships were investigated. The results of comparing the three methods showed that the k-NN method has better results than other regression methods due to its highest overall accuracy (83. 3%) and kappa coefficient (75. 9%) therefore, it was used to produce C-factor map. Results showed that the k-NN was very promising for mapping vegetation canopy cover in the arid and semi-arid areas. The results showed that among Vegetation indices NDVI had the highest correlation (0. 82) with percentage vegetation cover. Also, in the k-NN method, the Euclidean distance metrics in k = 9 has better results than the other two Fuzzy and Mahalanobis distances and can be used to estimation of vegetation cover map.

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APA: Copy

ZAKERI, EMAD, KARIMZADEH, HAMID REZA, & MOUSAVI, SEYED ALIREZA. (2021). Using k Nearest Neighbor (k-NN) algorithm as a suitable approach to estimate cover-management factor of RUSLE model in Shirin Dareh basin, North Khorasan. JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), 73(4 ), 753-770. SID. https://sid.ir/paper/397783/en

Vancouver: Copy

ZAKERI EMAD, KARIMZADEH HAMID REZA, MOUSAVI SEYED ALIREZA. Using k Nearest Neighbor (k-NN) algorithm as a suitable approach to estimate cover-management factor of RUSLE model in Shirin Dareh basin, North Khorasan. JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES)[Internet]. 2021;73(4 ):753-770. Available from: https://sid.ir/paper/397783/en

IEEE: Copy

EMAD ZAKERI, HAMID REZA KARIMZADEH, and SEYED ALIREZA MOUSAVI, “Using k Nearest Neighbor (k-NN) algorithm as a suitable approach to estimate cover-management factor of RUSLE model in Shirin Dareh basin, North Khorasan,” JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), vol. 73, no. 4 , pp. 753–770, 2021, [Online]. Available: https://sid.ir/paper/397783/en

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