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

Investigation of soil properties such as cation exchange capacity (CEC) and soil infiltration is an important role in environmental research. The measurement of these parameters is time-consuming and costly. In this study, intelligence-based models [artificial neural networks (MLP and RBF), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression (MR) techniques] are employed as alternatives to estimate the CEC and soil infiltration parameters from more readily available soil data. Two hundred soil samples were collected from soil 0–30 cm deep from two sites of the Ghoshe Region in Semnan Province, Iran. The first site was a flood plain and second site was agriculture land. The input data for models were electrical conductivity (EC), soil texture, lime percentage, sodium adsorption ratio (SAR), and bulk density. To evaluate the performance of these models, the statistical parameters such as root mean square error (RMSE), mean absolute error (MAE), mean error (ME), and coefficient of determination (R²) were used. Then the results of the intelligence-based models and MR were compared to each other’s. The results show that the MLP model was better than ANFIS, MR, and RBF models. Finally, sensitivity analysis was conducted to determine the most and the least influential variables affecting the soil infiltration and CEC parameters. It was found that EC and bulk density have respectively the most and the least effect on soil infiltration, and for CEC they were clay percentage and bulk density, respectively.     

Prediction of Soil Cation Exchange Capacity Using Different Soil Parameters by Intelligent Models

ABSTRACT

The traditional methods for the measurement of soil cation exchange capacity (CEC) are time-consuming and laborious. It is also difficult to maintain stability for long-term experiments and projects. Therefore, it is necessary to develop an indirect approach such as pedotransfer functions (PTFs) to estimate this property from more easily available soil data. The aim of this study was to compare multiple linear and nonlinear regression, classification and regression trees (C&RT), artificial neural network (ANN) model included multiple layer perceptron (MLP) and k-nearest neighbors (k-NN) to develop PTFs for predicting soil CEC. Soil samples, 929, were used into two subsets for training and testing of the models. Sensitivity and statistical analyzes were conducted to determine the most and the least influential variables affecting soil CEC. The prediction capability of models was assessed by statistical indicators included the normalized root-mean-square error (NRMSE) and the coefficient of determination (R²). Results of the present investigation showed that the k-NN and ANN models had the ability to estimate soil CEC by computing easily measurable variables with a guarantee of authenticity, reliability, and reproducibility. Therefore, the results of this study provide a superior basis for predicting soil CEC and could be applied to other parts of the world with similar challenges.     

Predicting Cationic Exchange Capacity in Calcareous Soils of East-Azerbaijan Province,Northwest Iran

The aim of this research is to study the efficiency of pedotransfer functions (PTFs) and artificial neural networks (ANNs) for cationic exchange capacity (CEC) prediction using readily available soil properties. Here, 417 soil samples were collected from the calcareous soils located in East-Azerbaijan province, northwest Iran and readily available soil properties, such as particle size distribution (PSD), organic matter (OM) and calcium carbonate equivalent (CCE), were measured. The entire 417 soil samples were divided into two groups, a training data set (83 soil samples) and test data set (334 soil samples). The performances of several published and derived PTFs and developed neural network algorithms using multilayer perceptron were compared, using a test data set. Results showed that, based on statistics of RMSE and R², PTFs and ANNs had a similar performance, and there was no significant difference in the accuracy of the model results. The result of the sensitivity analysis showed that the ANN models were very sensitive to the clay variable (due to the high variability of the clay). Finally, the models tested in this study could account for 85% of the variations in cationic exchange capacity (CEC) of soils in the studied area.

Abbreviations: ANN: arti?cial neural networks; MLP: multilayer perceptron; MLR: multiple linear regression; PTFs: Pedotransfer Functions; RBF: Radial Basis Function; MAE: mean absolute error; MSE: mean square error; CEC: cationic exchange capacity     

Selection of a Suitable Soft Computing Model for Estimation of Soil Cation Exchange Capacity

ABSTRACT

Measuring of soil cation exchange capacity (CEC) is difficult and time-consuming. Therefore, it is essential to develop an indirect approach such as pedotransfer functions (PTFs) to predict this property from more readily available soil data. The aim of this study was to compare multiple linear and nonlinear regression, adaptive neurofuzzy inference system, and an artificial neural network (ANN) model to develop PTFs for predicting soil CEC. One hundred and seventy-one soil samples were used into two subsets for training and testing of the models. The model's prediction capability was evaluated by statistical indicators that include RMSE, R², MBE, and RI. Results showed that the ANN model had the most reliable prediction when compared with other models. This study provides a strong basis for predicting soil CEC and identifying the most determinant properties influencing soil CEC in the north regions of Iran. Analytical framework results could be applied to other parts of the world with similar challenges.

Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Network; CEC: Cation Exchange Capacity; CV: Coefficient of Variation; FFBP: Feed-Forward Back-Propagation; FIS: Fuzzy Inference System; MBE: Mean Bias Error; MF: Membership Function; MLR: Multiple Linear Regressions; MNLR: Multiple Non-Linear Regressions; MLP: Multi-layer Perceptron; OC: Organic Carbon; PTFs: Pedotransfer Functions; R²: Determination Coefficient; RI: Relative Improvement; RMSE: Root Mean Square Error; SD: Standard Deviation     

Using support vector machines to predict cation exchange capacity of different soil horizons in Qingdao City,China

Agricultural, environmental and ecological modeling requires soil cation exchange capacity (CEC) that is difficult to measure. Pedotransfer functions (PTFs) are thus routinely applied to predict CEC from easily measured physicochemical properties (e.g., texture, soil organic matter, pH). This study developed the support vector machines (SVM)‐based PTFs to predict soil CEC based on 208 soil samples collected from A and B horizons in Qingdao City, Shandong Province, China. The database was randomly split into calibration and validation datasets in proportions of 3:1 using the bootstrap method. The optimal SVM parameters were searched by applying the genetic algorithm (GA). The performance of SVM models was compared to those of multiple stepwise regression (MSR) and artificial neural network (ANN) models. Results show that the accuracy of CEC predicted by SVM improves considerably over those predicted by MSR and ANN. The performance of SVM for B horizon (R² = 0.85) is slightly better than that for A horizon (R² = 0.81). The SVM is a powerful approach in the simulation of nonlinear relationship between CEC and physicochemical properties of widely distributed samples from different soil horizons. Sensitivity analysis was also conducted to explore the influence of each input parameter on the CEC predictions by SVM. The clay content is the most sensitive parameter, followed by soil organic matter and pH, while sand content has the weakest influence. This suggests that clay is the most important predictor for predicting CEC of both soil horizons.     

Comparative Analysis of MLR,ANN, and ANFIS Models for Prediction of Field Capacity and Permanent Wilting Point for Bafra Plain Soils

ABSTRACT

Soil hydraulic parameters like moisture content at field capacity and permanent wilting point constitute significant input parameters of various biophysical models and agricultural practices (irrigation timing and amount of irrigation to be applied). In this study, the performance of three different methods (Multiple linear regression – MLR, Artificial Neural Network – ANN and Adaptive Neuro-Fuzzy Inference System – ANFIS) with different input parameters in prediction of field capacity and permanent wilting point from easily obtained soil characteristics were compared. Correlation analysis indicated that clay content, sand content, cation exchange capacity, CaCO₃, and organic matter had significant correlations with FC and PWP (p < .01). Validation results revealed that the ANN model with the greatest R² and the lowest MAE and RMSE value exhibited better performance for prediction of FC and PWP than the MLR and ANFIS models. ANN model had R² = 0.83, MAE = 2.36% and RMSE = 3.30% for FC and R² = 0.81, MAE = 2.15%, RMSE = 2.89% for PWP in training dataset; R² = 0.80, MAE = 2.27%, RMSE = 3.12% for FC and R² = 0.83, MAE = 1.84%, RMSE = 2.40% for PWP in testing dataset. Also, Bayesian Regularization (BR) algorithm exhibited better performance for both FC and PWP than the other training algorithms.     

Automatic Recovery Estimation of Degraded Soils by Artificial Neural Networks in Function of Chemical and Physical Attributes in Brazilian Savannah Soil

ABSTRACT

The Oxisols is predominant in 54% of Brazilian territories and characterized by high weathering, relatively low chemical properties, and adequate structure. This study aimed to analyze the Oxisols through an Artificial Neural Network (ANN) with the purpose of estimating its recovery in function to soil chemical and physical attributes. The chemical attributes considered were: pH, cation exchange capacity (CEC), base saturation (V%), phosphorus (P), magnesium (Mg²⁺), and potassium (K⁺) and for the physical attributes, bulk density, soil porosity and soil resistance to penetration. The ANN used in this study is the Multilayer Perceptron (MLP), composed of three layers, input, intermediate and the output and with backpropagation training algorithm (supervised training). The intermediate layer is composed by 10 neurons and the layer of exit by 1 neuron, which has a function of informing the levels of chemical recovery (high, medium and low chemical attributes of the soil) and soil physics (recovered, partially recovered or not recovered). From the results obtained by ANN showed that the network reached an adequate training, with low mean square error (MSE). Therefore, ANN is a powerful and automatic alternative for the recovery estimation of degraded soils.     

Neural network models to predict cation exchange capacity in arid regions of Iran

Design and analysis of land‐use management scenarios requires detailed soil data. When such data are needed on a large scale, pedotransfer functions (PTFs) could be used to estimate different soil properties. Because existing regression‐based PTFs for estimating cation exchange capacity (CEC) do not, in general, apply well to arid areas, this study was conducted (i) to evaluate the existing models and (ii) to develop neural network‐based PTFs for predicting CEC in Aridisols of Isfahan in central Iran. As most researches have found a significant correlation between CEC and soil organic matter content (OM) and clay content, we also used these two variables for modelling of CEC. We tested several published PTFs and developed two neural network algorithms using multilayer perceptron and general regression neural networks based on a set of 170 soil samples. The data set was divided into two subsets for calibration and testing of the models. In general, the neural network‐based models provided more reliable predictions than the regression‐based PTFs.     

Cation exchange capacity variations in acidic forest soils from Sutton,Québec,Canada

Abstract

Single values of the cation exchange capacity (CEC) are widely used in modeling soil solution chemistry in soil and water ecosystems. Our aim was to determine the CEC as a function of pH and ionic strength in an acidic forest soil. We examined the cation exchange of two Humo‐Ferric Podzols (Haplorthods) equilibrated with artificial soil solutions of different concentrations. Aliquots of acid (HC1) or base (NaOH) were added to generate a pH range of 3 to 6. The CEC, determined by displacement with BaCl₂ showed little or no increase with increasing pH and a definite increase with lower pH. This anomalous behavior was attributed to the precipitation of aluminum (Al) at high pH and to its dissolution at low pH.     

Digital mapping of cation exchange capacity using genetic programming and soil depth functions in Baneh region,Iran

This study evaluates the performances of a combination of genetic programming and soil depth functions to map the three-dimensional distribution of cation exchange capacity (CEC) in a semiarid region located in Baneh region, Iran. Using the conditioned Latin hypercube sampling method, the locations of 188 soil profiles were selected, which were then sampled and analyzed. In general, results showed that equal-area quadratic splines had the highest R², 89%, in fitting the vertical CEC distribution compared to power and logarithmic functions with R² of 81% and 84%, respectively. Our findings indicated some auxiliary variables had more influence on the prediction of CEC. Normalized difference vegetation index (NDVI) had the highest correlation with CEC in the upper two layers. However, the most important auxiliary data for prediction of CEC in 30–60 cm and 60–100 cm were topographic wetness index and profile curvature, respectively. Validation of the predictive models at each depth interval resulted in R² values ranging from 66% (0–15 cm) to 19% (60–100 cm). Overall, results indicated the topsoil can be reasonably well predicted; however, the subsoil prediction needs to be improved. We can recommend the use of the developed methodology in mapping CEC in other parts in Iran.     

红松人工林土壤阳离子交换量空间分布及其组成演变

以长白山红松人工林为研究对象,运用对比分析方法,对不同林龄红松人工林土壤阳离子交换量空间分布及其组成进行研究。结果表明：红松人工林土壤阳离子交换量和盐基饱和度均随着林龄增加而减小;土壤交换性,氢和交换性铝离子含量则相反,随林龄增大而增加;土壤阳离子交换量、交换性钙、镁离子含量表层大于底层,土壤阳离子交换量及其组成根际土壤大于非根际土壤;土壤阳离子交换量、交换性钙、交换性镁、交换性钠红松阔叶混交林大于红松人工林。     

Improving on the Prediction of Cation Exchange Capacity for Highly Weathered and Structurally Contrasting Tropical Soils from Their Fine-Earth Fractions

The roles of fine-earth materials in the cation exchange capacity (CEC) of especially homogenous units of the kaolinitic and oxyhydroxidic tropical soils are still unclear. The CEC (pH 7) of some coarse-textured soils from southeastern Nigeria were related to their total sand, coarse sand (CS), fine sand (FS), silt, clay, and organic-matter (OM) contents before and after partitioning the dataset into topsoils and subsoils and into very-low-, low-, and moderate-/high-stability soils. The soil-layer categories showed similar CEC values; the stability categories did not. The CEC increased with decreasing CS but with increasing FS. Silt correlated negatively with the CEC, except in the moderate- to high-stability soils. Conversely, clay and OM generally impacted positively on the CEC. The best-fitting linear CEC function (R², 68%) was attained with FS, clay, and OM with relative contributions of 26, 38, and 36%, respectively. However, more reliable models were attained after partitioning by soil layer (R², 71–76%) and by soil stability (R², 81–86%). Notably FS's contribution to CEC increased while clay's decreased with increasing soil stability. Clay alone satisfactorily modeled the CEC for the very-low-stability soils, whereas silt contributed more than OM to the CEC of the moderate- to high-stability soils. These results provide new evidence about the cation exchange behavior of FS, silt, and clay in structurally contrasting tropical soils.     

Comparative study of three methods of determining cation exchange capacity of three major soils in the rainforest region of Southwestern Nigeria

The cation exchange capacity (CEC) of three benchmark soils in the rain forest region of Southwestern Nigeria, were measured by three standard CEC methods. Results obtained were compared with a view to selecting the best suited CEC method for the soils. The study sites were the Teaching and Research Farm of Obafemi Awolowo University, Ile-Ife, and Itagunmodi settlement. Two representative soil profile pits each were established in soils developed in coarse-grained granite and gneiss, fine-grained biotite gneiss and schist, and the amphibolite. The three methods gave different CEC values in the order of CEC-pH 8.2 > CEC-pH 7 > ECEC. There was a significant correlation between soil organic matter (SOM) and the CEC obtained by each of the three methods (P ≤ 0.05), meanwhile, total clay showed no significant correlation. ECEC provided the best estimate of the CEC for the soils and adequate SOM management is crucial to enhance sustainable productivity of the soils.     

Zinc desorption kinetics from some calcareous soils of orange (Citrus sinensis L.) orchards,southern Iran

Zinc (Zn) desorption is an important process to determine Zn bioavailability in calcareous soils. An experiment was performed to assess the pattern of Zn release from 10 calcareous soils of orange orchards, southern Iran and the soil properties influencing it. For Zn desorption studies, soil samples were extracted with diethylene triamine penta-acetic acid solution at pH 7.3 for periods of 0.083–48 h. Suitability of seven kinetic models was also investigated to describe Zn release from soils. Generally, Zn desorption pattern was characterized by a rapid initial desorption up to 2 h of equilibration, followed by a slower release rate. The simple Elovich and two-constant rate kinetic models described Zn release the best, so it seems that Zn desorption is probably controlled by diffusion phenomena. The values of the rate constants for the superior models were significantly correlated with some soil properties such as soil organic matter (SOM) content, cation exchange capacity (CEC), and soil pH, whereas carbonate calcium equivalent and clay content had no significant influence on Zn desorption from soils. SOM had a positive effect on the magnitude of Zn release from soils, while soil pH showed a negative effect on Zn desorption. Furthermore, the initial release rate of soil Zn is probably controlled by CEC in the studied soils. Finally, it could be concluded that SOM, CEC, and soil pH are the most important factors controlling Zn desorption from calcareous soils of orange orchards, southern Iran.

Abbreviations: Soil organic matter (SOM); Cation exchange capacity (CEC); Calcium carbonate equivalent (CCE); Zinc (Zn).     

Comparison of the prediction efficiency of two pedotransfer functions for soil cation‐exchange capacity

The aim of this study was to provide knowledge of the prediction efficiency of two pedotransfer functions for soil cation‐exchange capacity (CEC), i.e., the approaches of Krogh et al. (2000) and Scheinost (1995). Potential CEC, i.e., CEC at pH 8.1, was predicted for 30 samples of German soils showing a strong variation of soil organic C and clay content which are important soil characteristics for CEC. The results were compared to analyzed potential CEC of the samples by coefficient‐of‐efficiency (EC) criterion of Nash and Sutcliffe (1970). Significant deviations between observed and measured CEC were found for both functions. The approach of Scheinost (1995) showed a higher prediction efficiency (EC = 0.77 in comparison to EC = 0.26 for Krogh et al., 2000) and better results under extrapolative conditions.     

基于GIS和地理加权回归的砂田土壤阳离子交换量空间预测

土壤阳离子交换量(CEC)反映土壤保水保肥能力,研究CEC空间分布可为土壤改良和田间施肥提供理论依据。本文以宁夏香山地区砂田淡灰钙土为研究对象,在土壤CEC和理化性质相关分析基础上以普通克里格(OK)为对照,探索回归克里格(RK)和地理加权回归克里格(GWRK)在CEC空间插值上的应用,并对三者的插值精度及制图效果进行评价。描述统计表明研究区土壤CEC含量均值为10.145cmol/kg,CEC与有机质含量呈显著正相关,与砂粒含量呈显著负相关;地统计分析表明CEC实测值、OLS残差和GWR残差块金系数分别为8.50%、6.36%和7.02%,比值均小于25%,具有强烈空间自相关;对验证点进行插值精度分析,RK和GWRK的相对模型改进值(RI)分别为40.49%、41.50%,插值精度GWRKRKOK;从成图效果看,GWRK中辅助变量参与了局部回归,成图效果更加精细,揭示了更多空间变化细节。本研究结论可为土壤CEC空间预测研究提供可靠的方法借鉴。     

Development of ensemble pedotransfer functions for cation exchange capacity of soils of Qingdao in China

Soil cation exchange capacity (CEC), which is considered to be an indicator of buffering capacity, is an important soil attribute that influences soil fertility but is costly, time‐consuming and labour‐intensive to measure. Pedotransfer functions (PTFs) have routinely been used to predict soil CEC from easily measured soil properties, such as soil pH, texture and organic matter content. However, uncertainty in which one to select can be substantial as different PTFs do not necessarily produce the same result. In this study, a total of 100 soil samples were collected from surface horizons (0–20 cm) in different regions of Qingdao City, China. Three ensemble PTFs (ePTFs), including simple ensemble mean (SEM), individually bias‐removed ensemble mean (IBREM) and collective bias‐removed ensemble mean (CBREM), were developed to reduce the uncertainty in CEC prediction based on 12 published regression‐based PTFs. In addition, a local PTF (LPTF) for CEC was also developed using multiple stepwise regression and basic soil properties. The performances of the three ePTFs were compared with those of the published PTFs and LPTF. Results show that the differences between the performances of the published PTFs were substantial. When the systematic bias of each published PTF was removed separately, the prediction capability of the PTFs was increased. The performance of LPTF was significantly better than that of SEM, but slightly worse than IBREM. It is noted that CBREM had higher accuracy than all of the other methods. Overall, CBREM is a promising approach for estimating soil CEC in the study area.     

长期施肥下红壤旱地磷素有效性影响因子的冗余分析

以长期施肥(1988～2014年)的红壤旱地为研究对象,以全磷(TP)、有效磷(Bray-P)为响应变量,土壤pH值、大小粒级团聚体组成比例(PSAi)、铁铝氧化物、阳离子交换量(CEC)、分形维数(D)等指标为解释变量,借助于冗余分析(RDA)方法探讨各指标变量与红壤旱地TP和Bray-P含量变化的相关关系.研究结果...     

黄土高原小流域土壤阳离子交换量分布特征及影响因子

通过对黄土高原陕北地区3个小流域(朱家沟、纸坊沟和泥河沟)27个采样点的54个土壤样品分析,应用统计方法讨论土壤阳离子交换量的分布特征和影响因子。结果表明:(1)在3个所选定的典型小流域中,土壤CEC呈现明显的地带性,从北到南,CEC值显著升高。(2)在同一流域,CEC垂直地带变化基本表现为随高度降低而增大;而在同一剖面中,表层土壤CEC值总是高于下层。通过相关性分析和逐步回归检验,得出在粘土矿物类型基本相同的前提条件下,影响CEC值变化的主要因素有pH值、土壤黏粒含量和有机质含量,粉粒含量的影响较小,而砂粒含量则与CEC表现出显著负相关。     

Zur Verwendung von Gesteinsmehlen in der Landwirtschaft. II. Wirkung von Gesteinsmehlen als Bodenverbesserungsmittel

The use of rock powders in agriculture. II. Efficiency of rock powders for soil amelioration Five rock powders with different chemical and mineralogical characteristics were investigated in order to test their suitability for agricultural soil amelioration. The highest cation exchange capacity (CEC) was determined for the powder of smectite rich volcanic ash. Carbonate rock powders showed highest values for acid neutralization capacity (ANC). Silicate rock powders (granite, basalt) showed the lowest values for both investigated parameters. After some decades, a yearly application per hectare of 1000 kg of rock powder consisting of clay minerals or carbonates could at best successfully improve extreme poor soils, e.g. sandy soils with low humus content, by raising the CEC or the ANC. Rock powders rich in silicium, e.g. of granite, are not suitable to improve soils significantly.