Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran

Taghizadeh‐Mehrjardi, Ruhollah; Nabiollahi, Kamal; Minasny, Budiman; Triantafilis, John

doi:10.1016/j.geoderma.2015.04.008

Cited by 104 publications

(65 citation statements)

References 79 publications

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“…It is able to model non-linear relationships between predictors and the response variable to handle noise data (observations with missing covariate data) and other situations in which a small dataset is associated with a large number of covariates (Collard et al, 2014). Although RF has shown better performance for soil class mapping when compared to a set of other classifiers (Pahlavan-Rad et al, 2016;Heung et al, 2017), the studies performed by Collard et al (2014), Taghizadeh-Mehrjardi et al (2015), and Camera et al (2017) showed that MLR performed better than RF.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…Some studies addressed the comparison of data mining approaches for predicting soil classes by different predictive models. Nevertheless, despite the broad success of DSM techniques using tree-based model and logistic regression, few studies have compared the performance of promising classifiers for soil class mapping, such as logistic regression and Random Forest (Hengl et al, 2007;Collard et al, 2014;Taghizadeh-Mehrjardi et al, 2015;Pahlavan-Rad et al, 2016;Camera et al, 2017;Heung et al, 2017). Under similar conditions of western Haiti, this kind of studies is rarer.…”

Section: Introductionmentioning

confidence: 99%

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Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti

Jeune

Francelino

Souza

et al. 2018

Rev. Bras. Ciênc. Solo

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Digital soil mapping (DSM) has been increasingly used to provide quick and accurate spatial information to support decision-makers in agricultural and environmental planning programs. In this study, we used a DSM approach to map soils in western Haiti and compare the performance of the Multinomial Logistic Regression (MLR) with Random Forest (RF) to classify the soils. The study area of 4,300 km 2 is mostly composed of diverse limestone rocks, alluvial deposits, and, to a lesser extent, basalt. A soil survey was conducted whereby soils were described and classified at 258 sites. Soil samples were collected and subjected to physical and chemical analyses. Recursive Feature Elimination (RFE) was used to select the most important covariates from auxiliary data, such as climate, lithology, and morphometric properties to describe the soil-landscape relationship. Mapping performance was assessed by the Kappa index and overall accuracy derived from a confusion matrix generated using a 5-fold cross validation process. In addition, an external mapping validation was carried out using an independent soil dataset. Accordingly, the soil dataset was split into 80 % and 20 % for training and validation of the models, respectively. No significant statistical difference (Z = 0.56< |1.96|) was found between maps generated with both classifiers (Kappa index 0.45 for MLR and 0.42 for RF). Based on the Kappa values, the classification performance can be characterized as moderate for both algorithms. Surprisingly, the RF classifier outperformed MLR in the validation process (Kappa values of 0.55 and 0.33, respectively). These results suggest a higher generalization ability of RF. However, no significant statistical difference (Z = 1.83< |1.96|) was observed.The soil map derived from RF indicated the occurrence of Leptosols (48.5 %), Gleysols (19.6 %), Chernozems (8 %), and Fluvisols (6.6 %) in most of the study area. The DSM approaches proved suitable for mapping soils in western Haiti and could be used in other parts of the country, thereby closing information gaps with regard to Haitian soils.

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Section: Random Forest (Rf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti

Jeune

Francelino

Souza

et al. 2018

Rev. Bras. Ciênc. Solo

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“…Data from wir sensors for the job of physical movement recognition is taken for comparing the p AdaBoostM1 as the classifier of meta level with base level classifier C4.5 Graft in [9] classifier performance with optimization to categorize non-randomized readings and c biomedical quotations for text selection using organized reviews are studied in [10]. Cla Support vector machines, Conditional Random fields and Latent Dynamic conditional ran compared for user intention understanding in analysing web search engines was shown in [1 were analysed, sampled, selected and predicted for taxonomic soil class after investigating th power of data mining classifiers in [12]. Chi-Square Methods and R-Square techniques wer dimensional curve fitting using machine learning in [36].…”

Section: Support Vector Machines With Kernel Evaluationmentioning

confidence: 99%

“…Soil profiles were analysed, sampled, selected and predicted for taxonomic soil class after investigating the classification power of data mining classifiers in [12]. Chi-Square Methods and R-Square techniques were used for high dimensional curve fitting using machine learning in [36].…”

Section: Support Vector Machines With Kernel Evaluationmentioning

confidence: 99%

“…Classifiers namely Support vector machines, Conditional Random fields and Latent Dynamic conditional random fields are compared for user intention understanding in analysing web search engines was shown in [11]. Soil profiles were analysed, sampled, selected and predicted for taxonomic soil class after investigating the classification power of data mining classifiers in [12]. Chi-Square Methods and R-Square techniques were used for high dimensional curve fitting using machine learning in [36].…”

Section: Introductionmentioning

confidence: 99%

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Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Biju

Prashanth²

2017

Journal of Applied Computer Science Methods

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This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.

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Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity

Moghadas

Badorreck

2019

Near Surface Geophysics

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Soil water content (θ) is a key variable in different earth science disciplines since it mediates the water and energy exchange between the surface and atmosphere. Electrical and electromagnetic geophysical techniques have been widely used to estimate soil electrical conductivity (σ) and soil moisture. However, obtaining the σ−θ relationship is not straightforward due to the non‐linearity and also dependency on many different soil and environmental properties. The purpose of this paper is to determine if artificial neural network is an appropriate machine learning technique for relating electrical conductivity to soil water content. In this respect, time‐lapse electrical resistivity tomography measurements were carried out along a transect in the Chicken Creek catchment (Brandenburg, Germany). To ensure proper retrieval of the σ and θ, reference values were measured near the beginning of the transect via an excavated pit using 5TE capacitance sensors installed at different depths. We explored robustness and pertinence of the artificial neural network approach in comparison with Rhoades model (as a commonly used petrophysical relationship) to convert the inversely estimated σ from electrical resistivity tomography to the θ. The proposed approach was successfully validated and benchmarked by comparing the estimated values with the reference data. This study showed the superiority of the artificial neural network approach to the Rhoades model to obtain σ−θ relationship. In particular, artificial neural network allowed for more accurate estimation of the temporal wetting front than the petrophysical model. The proposed methodology thus offers a great promise for deriving spatiotemporal soil moisture patterns from geophysical data and obtaining the in situ σ−θ relationship, taking into account the non‐linear variations of the soil moisture.

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Comparing data mining classifiers to predict spatial distribution of USDA-family soil groups in Baneh region, Iran

Cited by 104 publications

References 79 publications

Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti

Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Machine learning to estimate soil moisture from geophysical measurements of electrical conductivity

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