Support vector machine and artificial neural network to model soil pollution: a case study in Semnan Province, Iran

Sakizadeh, Mohamad; Mirzaei, Rouhollah; Ghorbani, Hadi

doi:10.1007/s00521-016-2231-x

Cited by 38 publications

(11 citation statements)

References 48 publications

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“…The results for the two ML models showed that SVR performed slightly better than ANN, which was in agreement with the conclusions by Wijewardane et al (2016), Ottoy et al (2017) and Xu et al (2018) [43,71,75]. It was reported by prior studies that SVR can eliminate the local minimum issue of the error function of ANN [76,77]. Nevertheless, these evaluations were different from those that were obtained by Taghizadeh-Mehrjardi et al (2016, 2017), who showed better performances of ANN than SVR for soil attributes prediction [78,79].…”

Section: Comparison Between Intra-classes Of Three Dsm Techniquessupporting

confidence: 90%

A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content

Chen

Ren

et al. 2019

IJGI

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Accurate digital soil mapping (DSM) of soil organic carbon (SOC) is still a challenging subject because of its spatial variability and dependency. This study is aimed at comparing six typical methods in three types of DSM techniques for SOC mapping in an area surrounding Changchun in Northeast China. The methods include ordinary kriging (OK) and geographically weighted regression (GWR) from geostatistics, support vector machines for regression (SVR) and artificial neural networks (ANN) from machine learning, and geographically weighted regression kriging (GWRK) and artificial neural networks kriging (ANNK) from hybrid approaches. The hybrid approaches, in particular, integrated the GWR from geostatistics and ANN from machine learning with the estimation of residuals by ordinary kriging, respectively. Environmental variables, including soil properties, climatic, topographic, and remote sensing data, were used for modeling. The mapping results of SOC content from different models were validated by independent testing data based on values of the mean error, root mean squared error and coefficient of determination. The prediction maps depicted spatial variation and patterns of SOC content of the study area. The results showed the accuracy ranking of the compared methods in decreasing order was ANNK, SVR, ANN, GWRK, OK, and GWR. Two-step hybrid approaches performed better than the corresponding individual models, and non-linear models performed better than the linear models. When considering the uncertainty and efficiency, ML and two-step approach are more suitable than geostatistics in regional landscapes with the high heterogeneity. The study concludes that ANNK is a promising approach for mapping SOC content at a local scale.

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Section: Comparison Between Intra-classes Of Three Dsm Techniquessupporting

confidence: 90%

A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content

Chen

Ren

et al. 2019

IJGI

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“…In this study, the radial basis function (RBF) was used as the kernel function. The libsvm package [ 35 ] was imported into the MATLAB 2013b software to construct the SVM model and a multi-dimensional fitting relationship between soil nutrients content and the input PCs was established [ 36 , 37 , 38 , 39 ]. The SVM model parameters set as follows: the type of SVM was epsilon support vector regression (SVR); the meshgrid function was used to find optimum parameters, which including the parameter C of epsilon-SVR and gamma in kernel function; the epsilon in loss function of epsilon-SVR was 0.01 [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale

Song

Zhao

et al. 2018

Sensors

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Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP) and soil available potassium (AK) in Zengcheng, north of the Pearl River Delta, China. Hyperspectral remote sensing images (115 bands) of the Chinese Environmental 1A satellite were used as auxiliary variables and dimensionality reduction was performed using Pearson correlation analysis and principal component analysis. The TN, AP and AK of soil were predicted in the study area based on auxiliary variables after dimensionality reduction, along with stepwise linear regression (SLR), support vector machine (SVM), random forest (RF) and back-propagation neural network (BPNN) models; 324 independent points were used to verify the predictive performance. The BPNN model, which demonstrated the best predictive accuracy among all methods, combined ordinary kriging (OK) with mapping the spatial variations of soil nutrients. Results show that the BPNN model with double hidden layers had better predictive accuracy for soil TN (root mean square error (RMSE) = 0.409 mg kg−1, R2 = 44.24%), soil AP (RMSE = 40.808 mg kg−1, R2 = 42.91%) and soil AK (RMSE = 67.464 mg kg−1, R2 = 48.53%) compared with the SLR, SVM and RF models. The back propagation neural network-ordinary kriging (BPNNOK) model showed the best predictive results of soil TN (RMSE = 0.292 mg kg−1, R2 = 68.51%), soil AP (RMSE = 29.62 mg kg−1, R2 = 69.30%) and soil AK (RMSE = 49.67 mg kg−1 and R2 = 70.55%), indicating the best fitting ability between hyperspectral remote sensing bands and soil nutrients. According to the spatial mapping results of the BPNNOK model, concentrations of soil TN (north-central), soil AP (central and southwest) and soil AK (central and southeast) were respectively higher in the study area. The most important bands (464–517 nm) for soil TN (b10, b14, b20 and b21), soil AP (b3, b19 and b22) and soil AK (b4, b11, b12 and b25) exhibited the best response and sensitivity according to the SLR, SVM, RF and BPNN models. It was concluded that the application of hyperspectral images (visible-near-infrared data) with BPNNOK model was found to be an efficient method for mapping and monitoring soil nutrients at the regional scale.

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“…Next, to agriculture, mining activity is the other source contributing to the contamination of soil and groundwater with HMs in recent years [9,10].…”

Section: Field Laboratory Analysis and Statistical Analysismentioning

confidence: 99%

A New Method for Spatial Health Risk Assessment of Pollutants

Sakizadeh

Ghorbani

2017

IJT

Self Cite

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Support vector machine and artificial neural network to model soil pollution: a case study in Semnan Province, Iran

Cited by 38 publications

References 48 publications

A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content

A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content

Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale

A New Method for Spatial Health Risk Assessment of Pollutants

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