Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran

Taghizadeh‐Mehrjardi, Ruhollah; Nabiollahi, Kamal; Kerry, Ruth

doi:10.1016/j.geoderma.2015.12.003

Cited by 223 publications

(131 citation statements)

References 26 publications

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“…ACO ‐based ANFIS ). Similar results have been reported by the other researchers, who concluded that MLR was not successful for such prediction problems (Taghizadeh‐Mehrjardi et al ., ). For example, the RMSEs for clay prediction by ACO‐based ANFIS and ANN models were 24 and 23% less, respectively, than that obtained by the MLR model (Figure a).…”

Section: Resultsmentioning

confidence: 97%

“…() and Taghizadeh‐Mehrjardi et al. (), who reported that ANNs with one hidden layer could predict various soil properties successfully. The results of the ACO‐based ANN model in Table indicate that the optimal number of neurons in hidden layers for clay at the four standardized depths were 7, 6, 5 and 14, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…() and Taghizadeh‐Mehrjardi et al . (), who reported that the accuracy of prediction of the three‐dimensional mapping method decreased with depth.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, various data-mining methods have been used recently to predict soil properties at unsampled locations from environmental covariates (McBratney et al, 2003). Many of the methods use soft computing techniques, such as regression trees (Adhikari et al, 2013), random forests (Vaysse & Lagacherie, 2015), support vector regression (Ballabio, 2009) and artificial neural networks (ANN) (Taghizadeh-Mehrjardi et al, 2016). Another approach is the neuro-fuzzy inference system (ANIFIS), which integrates fuzzy logic and artificial neural networks (Jang, 1993).…”

Section: Introductionmentioning

confidence: 99%

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Predicting and mapping of soil particle‐size fractions with adaptive neuro‐fuzzy inference and ant colony optimization in central Iran

Taghizadeh‐Mehrjardi¹,

Toomanian

Khavaninzadeh³

et al. 2016

European J Soil Science

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In arid regions, knowledge of the variation in soil texture is crucial for land management because it affects soil physical, chemical, biological and most importantly hydrological properties. The availability of information on soil texture is scarce even though it is required to support land‐use management and sustainable development. Because it is costly to obtain information about the individual particle‐size fractions (PSFs), we used digital soil mapping methods (DSM) with environmental covariates that are less costly to obtain. Specifically, we explored the use of a digital elevation model and remote sensing data as environmental covariates to predict the vertical (i.e. 0–0.15, 0.15–0.3, 0.3–0.6 and 0.6–1 m) and lateral variation in PSFs over a 150‐km2 area in central Iran. We used a combination of equal‐area spline depth functions and three data‐mining techniques: multiple linear regression (MLR), artificial neural networks (ANN) and the neuro‐fuzzy inference system (ANFIS). In addition, we explored the effect of the reduction in dimension of feature space with ant colony optimization (ACO) and correlation‐based feature selection (CFS) on the accuracy of prediction of spatial models for each PSF. The results showed that the prediction of clay at 0–0.15‐m depth with ACO indicated the importance of including Landsat ETM+, the digital numbers of band 7 of Landsat images (B7) and clay index, whereas at 0.60–1‐m depth the wetness index and multi‐resolution valley bottom flatness index (MRVBF) were important. Model evaluation by leave‐one‐out cross‐validation with 191 soil observations indicated that the predictions by the ACO‐based ANFIS model (RMSE = 4.51% and R2 = 0.74 for clay at 0–0.15‐m) were more accurate than those by MLR and ANN. Spatial prediction was also better for the topsoil (0–0.15‐m) than at depth (RMSE = 7.1% for clay at 0.6–1 m); therefore, we conclude that the environmental covariates tested cannot resolve subsurface variation as accurately. Nevertheless, we recommend prediction by the ACO‐based ANFIS model and splines of lateral and vertical distribution of PSFs in other arid regions of Iran with the same agro‐ecological conditions. Highlights Digital soil mapping of particle size‐fractions (PSF) by adaptive neuro‐fuzzy inference and ant colony optimization. Use of ant colony optimization (ACO) to assist in feature selection of environmental covariates. Neuro‐fuzzy inference system (ANFIS) superior to multiple linear regression (MLR) and artificial neural networks (ANN). PSF prediction by ACO‐based ANFIS model and splines is optimal.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

“…() and Taghizadeh‐Mehrjardi et al . (), who reported that the accuracy of prediction of the three‐dimensional mapping method decreased with depth.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting and mapping of soil particle‐size fractions with adaptive neuro‐fuzzy inference and ant colony optimization in central Iran

Taghizadeh‐Mehrjardi¹,

Toomanian

Khavaninzadeh³

et al. 2016

European J Soil Science

Self Cite

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“…Li et al, 2011;Liess et al, 2012;Adhikari et al, 2013;Vaysse and Lagacherie, 2015;Were et al, 2015;Lacoste et al, 2016;Mulder et al, 2016;Somarathna et al, 2016;Taghizadeh-Mehrjardi et al, 2016;Yang et al, 2016). Geodata availability and deemed importance often determine what covariates are used for DSM.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of digital soil mapping approaches with large sets of environmental covariates

Nussbaum¹,

Spiess²,

Baltensweiler³

et al. 2018

SOIL

214

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Abstract. The spatial assessment of soil functions requires maps of basic soil properties. Unfortunately, these are either missing for many regions or are not available at the desired spatial resolution or down to the required soil depth. The field-based generation of large soil datasets and conventional soil maps remains costly. Meanwhile, legacy soil data and comprehensive sets of spatial environmental data are available for many regions.Digital soil mapping (DSM) approaches relating soil data (responses) to environmental data (covariates) face the challenge of building statistical models from large sets of covariates originating, for example, from airborne imaging spectroscopy or multi-scale terrain analysis. We evaluated six approaches for DSM in three study regions in Switzerland (Berne, Greifensee, ZH forest) by mapping the effective soil depth available to plants (SD), pH, soil organic matter (SOM), effective cation exchange capacity (ECEC), clay, silt, gravel content and fine fraction bulk density for four soil depths (totalling 48 responses). Models were built from 300-500 environmental covariates by selecting linear models through (1) grouped lasso and (2) an ad hoc stepwise procedure for robust external-drift kriging (georob). For (3) geoadditive models we selected penalized smoothing spline terms by component-wise gradient boosting (geoGAM). We further used two tree-based methods: (4) boosted regression trees (BRTs) and (5) random forest (RF). Lastly, we computed (6) weighted model averages (MAs) from the predictions obtained from methods 1-5.Lasso, georob and geoGAM successfully selected strongly reduced sets of covariates (subsets of 3-6 % of all covariates). Differences in predictive performance, tested on independent validation data, were mostly small and did not reveal a single best method for 48 responses. Nevertheless, RF was often the best among methods 1-5 (28 of 48 responses), but was outcompeted by MA for 14 of these 28 responses. RF tended to over-fit the data. The performance of BRT was slightly worse than RF. GeoGAM performed poorly on some responses and was the best only for 7 of 48 responses. The prediction accuracy of lasso was intermediate. All models generally had small bias. Only the computationally very efficient lasso had slightly larger bias because it tended to under-fit the data. Summarizing, although differences were small, the frequencies of the best and worst performance clearly favoured RF if a single method is applied and MA if multiple prediction models can be developed.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Climate Change and Agriculture

Imran¹,

Benkeblia²,

Amanullah³

et al. 2022

Climate Change and Agriculture

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Digital mapping of soil organic carbon at multiple depths using different data mining techniques in Baneh region, Iran

Cited by 223 publications

References 26 publications

Predicting and mapping of soil particle‐size fractions with adaptive neuro‐fuzzy inference and ant colony optimization in central Iran

Predicting and mapping of soil particle‐size fractions with adaptive neuro‐fuzzy inference and ant colony optimization in central Iran

Evaluation of digital soil mapping approaches with large sets of environmental covariates

Climate Change and Agriculture

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