Estimation of Soil Infiltration and Cation Exchange Capacity Based on Multiple Regression, ANN (RBF, MLP), and ANFIS Models

Kashi, Hamed; Emamgholizadeh, Samad; Ghorbani, Hadi

doi:10.1080/00103624.2013.874029

Cited by 66 publications

(19 citation statements)

References 23 publications

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“…Some researchers have also confirmed high efficiency of the ANFIS model in DSM. Kashi et al [47] estimated CEC and water infiltration in the soil using both ANFIS and ANN, and the results indicated the higher efficiency of the ANFIS model. Besalatpour et al [48] used the ANN and ANFIS models to predict wet soil stability from several auxiliary variables and their results showed very high potentials for ANN and ANFIS models.…”

Section: Neuro-fuzzy Technique (Anfis)mentioning

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: Neuro-fuzzy Technique (Anfis)mentioning

confidence: 99%

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

et al. 2019

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“…Methods developed to measure and quantify water infiltration in soil are generally time-consuming and costly. Therefore, PTFs have been developed and applied by many researchers -e.g., Jemsi et al (2013) Kashi et al (2014), Sarmadian and Taghizadeh-Mehrjardi (2014), and Rahmati (2017) -in order to easily parameterize infiltration models. However, these PTFs have been developed for specific regions, often limiting their applicability.…”

Section: Introductionmentioning

confidence: 99%

Development and analysis of the Soil Water Infiltration Global database

Rahmati

Weihermüller²,

Vanderborght³

et al. 2018

Earth Syst. Sci. Data

118

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Abstract. 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.

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“…Si et al (2015) also compared the performance of ANFIS and two kinds of ANN to predict water content; they concluded that ANFIS was the most accurate. Kashi et al (2014) applied ANFIS to estimate soil infiltration and cation exchange capacity.…”

Section: Introductionmentioning

confidence: 99%

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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Estimation of Soil Infiltration and Cation Exchange Capacity Based on Multiple Regression, ANN (RBF, MLP), and ANFIS Models

Cited by 66 publications

References 23 publications

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

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

Development and analysis of the Soil Water Infiltration Global database

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

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