Comparison of data mining techniques to predict and map the Atterberg limits in central plateau of Iran

The Atterberg limits display soil mechanical behavior and, therefore, can be so important for topics related to soil management. The aim of the research was to investigate the spatial variability of the Atterberg limits using three most common digital soil-mapping techniques, the pool of easy-to-obtain environmental variables and 85 soil samples in central Iran. The results showed that the maximum amount of liquid limit (LL) and plastic limit (PL) were obtained in the central, eastern and southeastern parts of the study area where the soil textural classes were loam and clay loam. The minimum amount of LL and PL were related to the northwestern parts of the study area, adjacent to the mountain regions, where the samples had high levels of sand content (>80%). The ranges of plasticity index (PI) in the study area were obtained between 0.01 to 4%. According to the leave-in-out cross-validation method, it should be highlighted the combination of artifiial bee colony algorithm (ABC) and artifiial neural network (ANN) techniques were the best model to predict the Atterberg limits in the study area, compared to the support vector machine and regression tree model. For instance, ABC-ANN could predict PI with RMSE, R2 and ME of 0.23, 0.91 and -0.03, respectively. Our fiding generally indicated that the proposed method can explain the most of variations of the Atterberg limits in the study area, and it could berecommended, therefore, as an indirect approach to assess soil mechanical properties in the arid regions, where the soil survey/sampling is difficult to undertake

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.Comment: 30pages, 9 figure

Prediction of soil macronutrients using fractal parameters and artificial intelligence methods

Aim of study: To evaluate artificial neural networks (ANN), and k-Nearest Neighbor (k-NN) to support vector regression (SVR) models for estimation of available soil nitrogen (N), phosphorous (P) and available potassium (K).Area of study: Two separate agricultural sites in Semnan and Gorgan, in Semnan and Golestan provinces of Iran, respectively.Material and methods: Complete data set of soil properties was used to evaluate the models’ performance using a k-fold test data set scanning procedures. Soil property measures including clay, sand and silt content, soil organic carbon (SOC), electrical conductivity (EC), lime content as well as fractal dimension (D) were used for the prediction of soil macronutrients. A Gamma test was utilized for defining the optimum combination of the input variables.Main results: The sensitivity analysis showed that OC, EC, and clay were the most significant variables in the prediction of soil macronutrients. The SVR model was more accurate compared to the ANN and k-NN models. N values were estimated more accurately than K and P nutrients, in all the applied models.Research highlights: The accuracy of models among the test stages illustrated that using a single data set for investigation of model performance could be misleading. Therefore, the complete data set would be necessary for suitable evaluation of the model

Estimativa da composição elementar de solos do Azerbaijão oeste, Irã, utilizando-se modelos espectrais de infravermelho

[Abstract] Characterizing the elemental composition provides useful information about the weathering degree of soils. In Miandoab County, Northern Iran, this characterization was missing, and thus the objectives of this work were to evaluate the weathering degrees for the most typical soils in the area from their elemental compositions, and to estimate this elemental composition using Fourier transform infrared spectroscopy and Random Forest models. Five soil profiles, including Aridisols and Inceptisols, were selected as the most representative of the area. Major elemental oxides were determined in each genetic horizon by X-ray fluorescence, showing that these soils were at early developmental stages. Only Al2O3 and CaO were accurately estimated, with R2 values of 0.8, and out-of-bag mean square errors of 0.2 and 1.1, respectively. The other oxides were not predicted satisfactorily, probably due to small differences in their elemental compositions. Random Forest provided the important spectral bands related to the content of each element. For Al2O3, these bands were between 500 and 650 cm-1, which represent out-of-plane OH bending vibrations and Al-O gibbsite and alumino-silicate vibrations. For CaO, the most important bands are related to carbonate content. A combination of Fourier transform infrared spectra and Random Forest models can be used as a rapid and low-cost technique to estimate the elemental composition of arid and semi-arid soils of Northern Iran.[Resumo] A caracterização da composição elementar fornece informações úteis para caracterizar o grau de alteração dos solos. Em Miandoab, norte do Irã, esta caracterização não existe. Os objetivos deste trabalho foram avaliar o grau de intemperismo dos solos típicos da região usando a sua composição elementar e estimar esta composição usando espectroscopia infravermelha com transformada de Fourier (FTIR) e modelos Random Forest (RF). Foram selecionados cinco perfis de solo, incluindo Aridisolos e Inceptisolos, como os mais representativos da área. Os principais óxidos elementares foram determinados por fluorescência de raios-X em cada horizonte genético, mostrando que estes solos estavam em um estágio de baixo grau de desenvolvimento. Apenas o Al2O3 e o CaO foram estimados com precisão, com valores de R2 de 0,8 e erro quadrático médio nos dados utilizados para validação de 0,2 e 1,1, respectivamente, enquanto os outros óxidos não foram preditos satisfatoriamente, provavelmente devido às pequenas diferenças na sua composição. O modelo Random Forest forneceu importantes bandas espectrais relacionadas com o conteúdo de cada elemento. Para o Al2O3, estes atingiram a região 500 a 650 cm-1, o que foi atribuído a vibrações de flexão de OH e vibrações de Al-O de gibbsita e alumino-silicatos. Para o CaO, as bandas mais importantes estavam relacionadas ao teor de carbonatos. Os resultados indicam que uma combinação de espectros infravermelha de transformada de Fourier e modelos Random Forest pode ser usada como uma técnica rápida e de baixo custo para estimar a composição elementar de solos do norte do Irã

Development and analysis of the Soil Water Infiltration Global database

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it