Assessing the performance of decision tree and neural network models in mapping soil properties
Autor: | Ahmad Heidari, Payam Dolati, Ali Asghar Zolfaghari, Fatemeh Hateffard |
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Rok vydání: | 2019 |
Předmět: |
Global and Planetary Change
010504 meteorology & atmospheric sciences Geography Planning and Development Elevation Decision tree Geology Soil science Terrain Silt 010502 geochemistry & geophysics 01 natural sciences Pearson product-moment correlation coefficient Environmental data symbols.namesake Principal component analysis symbols Environmental science Digital elevation model 0105 earth and related environmental sciences Nature and Landscape Conservation Earth-Surface Processes |
Zdroj: | Journal of Mountain Science. 16:1833-1847 |
ISSN: | 1993-0321 1672-6316 |
Popis: | To build any spatial soil database, a set of environmental data including digital elevation model (DEM) and satellite images beside geomorphic landscape description are essentials. Such a database, integrates field observations and laboratory analyses data with the results obtained from qualitative and quantitative models. So far, various techniques have been developed for soil data processing. The performance of Artificial Neural Network (ANN) and Decision Tree (DT) models was compared to map out some soil attributes in Alborz Province, Iran. Terrain attributes derived from a DEM along with Landsat 8 ETM+, geomorphology map, and the routine laboratory analyses of the studied area were used as input data. The relationships between soil properties (including sand, silt, clay, electrical conductivity, organic carbon, and carbonates) and the environmental variables were assessed using the Pearson Correlation Coefficient and Principle Components Analysis. Slope, elevation, geomforms, carbonate index, stream network, wetness index, and the band’s number 2, 3, 4, and 5 were the most significantly correlated variables. ANN and DT did not show the same accuracy in predicting all parameters. The DT model showed higher performances in estimating sand (R2=0.73), silt (R2=0.70), clay (R2=0.72), organic carbon (R2=0.71), and carbonates (R2=0.70). While the ANN model only showed higher performance in predicting soil electrical conductivity (R2=0.95). The results showed that determination the best model to use, is dependent upon the relation between the considered soil properties with the environmental variables. However, the DT model showed more reasonable results than the ANN model in this study. The results showed that before using a certain model to predict variability of all soil parameters, it would be better to evaluate the efficiency of all possible models for choosing the best fitted model for each property. In other words, most of the developed models are site-specific and may not be applicable to use for predicting other soil properties or other area. |
Databáze: | OpenAIRE |
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