DIGITAL MAPPING OF SOIL TEXTURE USING REGRESSION TREE AND ARTIFICIAL NEURAL NETWORK IN BIJAR, KURDISTANK

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NABIOLLAHI K.; HAIDARI A.; TAGHIZADEH MEHRJERDI R.

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Journal: Journal of Water and Soil Year:2014 | Volume:28 | Issue:5 Page(s): 1025-1036

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Information Journal Paper

Title

DIGITAL MAPPING OF SOIL TEXTURE USING REGRESSION TREE AND ARTIFICIAL NEURAL NETWORK IN BIJAR, KURDISTANK

Author(s)

NABIOLLAHI K. | HAIDARI A. | TAGHIZADEH MEHRJERDI R. | Issue Writer Certificate

Keywords

AUXILIARY DATAQ1

SPATIAL VARIATIONQ2

DIGITAL SOIL MAPPINGQ1

Abstract

Soil texture is an important soil physical property that governs most physical, chemical, biological, and hydrological processes in soils. Detailed information on soil texture variability is crucial for proper crop and land management and environmental studies. Therefore, at present research, 103 soil profiles were dogged and then sampled in order to prepare digital map of soil texture in Bijar, Kurdistan. AUXILIARY DATA used in this study torepresent predictive soil forming factors were terrain attributes, Landsat 7 ETM+ data and a geomorphologic surfaces map. To make a relationship between the soil data set (i.e. Clay, sand and silt) and AUXILIARY DATA, regression tree (RT) and artificial neural network (ANN) were applied. Results showed that the RT had the higher accuracy than ANN for spatial prediction of three parameters. For the clay fraction, determination of coefficient (R2) and root mean square root (RMSE) calculated for two models were 0.46, 0.81 and 17.10, 12.50, based on validation data set (20%). Our results showed some auxiliary variables had more influence on predictive soil class model which included: geomorphology map, wetness index, multi-resolution index of valley bottom flatness, elevation, slope length, and B3. In general, results showed that decision tree models had high eraccuracy than ANN models and also their results are more convenient for interpretation. Therefore, it issug gested using of decision tree models for spatial prediction of soil properties in future studies.

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APA: Copy

NABIOLLAHI, K., HAIDARI, A., & TAGHIZADEH MEHRJERDI, R.. (2014). DIGITAL MAPPING OF SOIL TEXTURE USING REGRESSION TREE AND ARTIFICIAL NEURAL NETWORK IN BIJAR, KURDISTANK. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), 28(5), 1025-1036. SID. https://sid.ir/paper/141135/en

Vancouver: Copy

NABIOLLAHI K., HAIDARI A., TAGHIZADEH MEHRJERDI R.. DIGITAL MAPPING OF SOIL TEXTURE USING REGRESSION TREE AND ARTIFICIAL NEURAL NETWORK IN BIJAR, KURDISTANK. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)[Internet]. 2014;28(5):1025-1036. Available from: https://sid.ir/paper/141135/en

IEEE: Copy

K. NABIOLLAHI, A. HAIDARI, and R. TAGHIZADEH MEHRJERDI, “DIGITAL MAPPING OF SOIL TEXTURE USING REGRESSION TREE AND ARTIFICIAL NEURAL NETWORK IN BIJAR, KURDISTANK,” JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), vol. 28, no. 5, pp. 1025–1036, 2014, [Online]. Available: https://sid.ir/paper/141135/en

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