Artificial Neural Network and Decision Tree in Predictive Soil Mapping of Hoi Num Rin Sub-Watershed, Thailand

Moonjun, R.; Farshad, A.; Shrestha, Dhruba Pikha; Vaiphasa, Chaichoke

doi:10.1007/978-90-481-8863-5_13

Cited by 22 publications

(10 citation statements)

References 6 publications

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“…Kheir et al [44] used an RT model for soil mapping. Moonjun et al [45] compared the technique of ANN and RT to predict soil units and showed that there is no significant difference between the two techniques for prediction. Table 1).…”

Section: Effective Auxiliary Datamentioning

confidence: 99%

The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran

et al. 2019

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To predict the soil texture fractions, 115 profiles were identified based on the Latin hypercube sampling technique, the horizons were sampled, and the clay, sand, and silt contents (in percentages) of soil samples were measured. Then equal-area quadratic spline depth functions were used to derive clay, sand, and silt contents at five standard soil depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm). Auxiliary variables used in this study include the terrain attributes (derived from a digital elevation model), Landsat 8 image data (acquired in 2015), geomorphological map, and spectrometric data (laboratory data). Artificial neural network (ANN), regression tree (RT), and neuro-fuzzy (ANFIS) models were used to make a correlation between soil data (clay, sand, and silt) and auxiliary variables. The results of this study showed that the ANFIS model was more accurate in the prediction of the three parameters of clay, silt, and sand than ANN and RT. Moreover, the ability of ANFIS model to estimate the soil texture fractions in the surface layers was higher than the lower layers. The mean coefficient of determination (R2) values calculated by 10-fold cross validation suggested the higher prediction performance in the upper depth intervals and higher prediction error in the lower depth intervals (e.g., R2 = 0.91, concordance correlation coefficient (CCC) = 0.90, RMSE = 4.00 g kg−1 for sand of 0–5 cm depth, and R2 = 0.68, CCC = 0.60, RMSE = 8.03 g kg−1 for 60–100 cm depth). The results also showed that the most important auxiliary variables are spectrometric data, multi-resolution, valley-bottom flatness index and wetness index. Overall, it is recommended to use ANFIS models for the digital mapping of soil texture fractions in other arid regions of Iran.

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Section: Effective Auxiliary Datamentioning

confidence: 99%

The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran

et al. 2019

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“…MLP creates a model that maps the input to the output using training data, so that the model can be used to predict the output when it is unknown. In the present work, and after some primary tests to select the model, multilayer feed forward back-propagation ANN was applied (as 15 used by Behrens et al, 2005;Moonjun, 2007), using Statistica 8.0 software. The BFGS (Broyden-Fletcher-Goldfarb-Shanno) and Scaled Conjugate Gradient algorithms were used to train the ANN (Bishop, 1995).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Digital Soil Mapping Using Artificial Neural Networks and Terrain-Related Attributes

et al. 2015

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25Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANN) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used 30 test and validation areas to calculate accuracies of interpolated and extrapolated data. The results show that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, lower errors were observed with the WRB classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (seven soils in the case of ST vs. five with WRB). Training errors were below 11% for all the ANN models 35applied, while the test (interpolation error) and validation (extrapolation error) errors were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology, as soil-forming factors, should be used as ANN input data. 40

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“…Almost all studies in which categorical map units were disaggregated considered a smaller number of classes (i.e. less than 10 classes: Behrens et al, 2010;Brus et al, 2008;Sun et al, 2011;10 to 20 classes: Debella-Gilo and Etzelmüller, 2009;Hengl et al, 2007;Kempen et al, 2009;Moonjun et al, 2010; more than 20 classes: Grinand et al, 2008;Smith et al, 2010;Stum et al, 2010). Only Smith et al (2010) predicted more than 100 classes, however not in a single model but with knowledge-based fuzzy classification rules for every class separately.…”

Section: Introductionmentioning

confidence: 97%