APPLICATION OF RANDOM FOREST METHOD FOR PREDICTING SOIL CLASSES IN LOW RELIEF LANDS (CASE STUDY: HIRMAND COUNTY)

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MIRAKZEHI KH.; SHAHRIARI A.; PAHLAVAN RAD M.R.; BAMERI A.

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Journal: Journal of Water and Soil Conservation Year:2017 | Volume:24 | Issue:1 Page(s): 67-84

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Title

APPLICATION OF RANDOM FOREST METHOD FOR PREDICTING SOIL CLASSES IN LOW RELIEF LANDS (CASE STUDY: HIRMAND COUNTY)

Author(s)

MIRAKZEHI KH. | SHAHRIARI A. | PAHLAVAN RAD M.R. | BAMERI A. | Issue Writer Certificate

Keywords

SOIL DIGITAL MAPPINGQ1

RANDOM FOREST TECHNIQUEQ1

MAP ACCURACYQ1

ARID REGIONSQ1

SISTAN PLAINQ1

Abstract

Background and Objectives: Soil survey and mapping are base of soil information for environmental modeling as a way to determine soil distribution patterns, describe and display it to understood and interpreted for different users. Digital soil mapping creates link between classes or soil characteristics and environmental factors affected soil formation and development by using mathematical models which can provide more precise and accurate soil maps and reducing costs of soil survey and mapping projects. This study was done to mapping soil great groups and subgroups by using RANDOM FOREST TECHNIQUE in the Hirmand county lands in SISTAN PLAIN.Materials and Methods: In this study 108 soil profiles were dug on about 60.000 hectares of Hirmand county lands. Sixteen environmental variables were used as estimators for soil mapping including land properties, salinity and vegetation index. After classification of soil profiles to great groups and subgroups, soil classes map produced by using random forest (RF) method. It should be mentioned 80 percent of data was used for model training and 20 percent for independent validation.Results: Pedological studies showed soils of SISTAN PLAIN haven’t high development and most of them are Entisols and Aridisols. Most soil profiles classified as Torrifluvents on great groups level and Typic Torrifluvents as a subgroup. Also the result of RF showed the lowest out of bag error (OOB) samples in soil great groups and subgroups were 43.53 and 50.59 respectively.Independent validation results showed the best accuracy obtained for soil great groups and subgroups were 48 and 53 percent respectively. Valley depth, convergence index, channel network between and salinity in soil great groups and valley depth, elevation and catchment area in soil subgroups were the most important environmental variables to estimate soil classes.Conclusion: The results showed most soils are young in the low relief lands in ARID REGIONS and these regions have also low soil diversity. SOIL DIGITAL MAPPING and RANDOM FOREST TECHNIQUE could be useful for soil classes prediction and soil mapping in this kind of regions.

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APA: Copy

MIRAKZEHI, KH., SHAHRIARI, A., PAHLAVAN RAD, M.R., & BAMERI, A.. (2017). APPLICATION OF RANDOM FOREST METHOD FOR PREDICTING SOIL CLASSES IN LOW RELIEF LANDS (CASE STUDY: HIRMAND COUNTY). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 24(1), 67-84. SID. https://sid.ir/paper/156564/en

Vancouver: Copy

MIRAKZEHI KH., SHAHRIARI A., PAHLAVAN RAD M.R., BAMERI A.. APPLICATION OF RANDOM FOREST METHOD FOR PREDICTING SOIL CLASSES IN LOW RELIEF LANDS (CASE STUDY: HIRMAND COUNTY). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2017;24(1):67-84. Available from: https://sid.ir/paper/156564/en

IEEE: Copy

KH. MIRAKZEHI, A. SHAHRIARI, M.R. PAHLAVAN RAD, and A. BAMERI, “APPLICATION OF RANDOM FOREST METHOD FOR PREDICTING SOIL CLASSES IN LOW RELIEF LANDS (CASE STUDY: HIRMAND COUNTY),” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 24, no. 1, pp. 67–84, 2017, [Online]. Available: https://sid.ir/paper/156564/en

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