Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

Wang, Shijie; Bai, Xiaoyong; Zhou, Dong; Luo, Geping; Wang, Jinfeng; Wang, Mingming; Lu, Qian; Yang, Yujie; Hu, Zhang; Li, Chao‐Jun; Deng, Yuanhong

doi:10.3390/su11113197

Cited by 17 publications

(8 citation statements)

References 51 publications

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“…Soil arsenic content and spectral reflectance may often have a complex nonlinear relationship [38], so the PLSR method has poor performance in some cases. The neural network algorithm has excellent performance and efficiency, and has excellent performance in solving complex nonlinear problems [39][40][41][42], but it is prone to lack of generalization ability. Therefore, it is combined with SFLA's excellent global search ability to optimize the initial parameters of RBFNN, so as to obtain a model with better fitting ability and prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

Wei

Wang

et al. 2020

Sensors

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With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.

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Section: Discussionmentioning

confidence: 99%

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

Wei

Wang

et al. 2020

Sensors

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“…(Liu et al, 2019b) used PSO-BPNN to predict concentrations of Hg, Cd and As with higher accuracy than primary BPNN. Tian et al (2019) optimized BPNN with the combination of GA and the ant colony algorithm to predict heavy metal concentration, with a reported R 2 value for Cr detection (0.87) higher than for primary BPNN (0.55).…”

Section: Neural Networkmentioning

confidence: 99%

VIRS based detection in combination with machine learning for mapping soil pollution

Jia

O’Connor

Shi

et al. 2021

Environmental Pollution

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“…One group was used for the establishment of the model, and is referred to as the calibration set, and the other group was used to test the predictability of the model, and is called the validation set. In this study, the gradient concentration method does not take into account the influence of spectral vectors, while the Kennard-Stone (KS) method does not take into account the concentration vectors [42]. In order to effectively cover the multidimensional vector space and improve the predictive ability of the established model, both the spectral vectors and concentration vectors were taken into account when partitioning the calibration set and validation set of samples.…”

Section: Experiments and Analysismentioning

confidence: 99%

Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy

Wei

Zhang

et al. 2019

Sensors

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: In this study, in order to solve the difficulty of the inversion of soil arsenic (As) content using laboratory and field reflectance spectroscopy, we examined the transferability of the prediction method. Sixty-three soil samples from the Daye city area of the Jianghan Plain region of China were taken and studied in this research. The characteristic wavelengths of soil As content were then extracted from the full bands based on iteratively retaining informative variables (IRIV) coupled with Spearman’s rank correlation analysis (SCA). Firstly, the IRIV algorithm was used to roughly select the original spectral data. Gaussian filtering (GF), first derivative (FD) filtering, and gaussian filtering again (GFA) pretreatments were then used to improve the correlation between the spectra and soil As content. A subset with absolute correlation values greater than 0.6 was then retained as the optimal subset after each pretreatment. Finally, partial least squares regression (PLSR), Bayesian ridge regression (BRR), ridge regression (RR), kernel ridge regression (KRR), support vector machine regression (SVMR), eXtreme gradient boosting (XGBoost) regression, and random forest regression (RFR) models were used to estimate the soil As values using the different characteristic variables. The results showed that, compared with the traditional method based on IRIV, using the characteristic bands selected by the IRIV-SCA method can effectively improve the prediction accuracy of the models. For the laboratory spectra experiment stage, the six most representative characteristic bands were selected. The performance of IRIV-SCA-SVMR was found to be the best, with the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) in the validation set being 0.97, 0.22, and 0.11, respectively. For the field spectra experiment stage, the 12 most representative characteristic bands were selected. The performance of IRIV-SCA-XGBoost was found to be the best, with the R2, RMSE, and MAE in the validation set being 0.83, 0.35, and 0.29, respectively. The accuracy and stability of the inversion of soil As content are significantly improved by the use of the proposed method, and the method could be used to provide accurate data for decision support for the treatment and recovery of As pollution over a large area.

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Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

Cited by 17 publications

References 51 publications

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing

VIRS based detection in combination with machine learning for mapping soil pollution

Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy

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