scholarly journals Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra

2020 ◽  
Vol 12 (11) ◽  
pp. 4441
Author(s):  
Yun Xue ◽  
Bin Zou ◽  
Yimin Wen ◽  
Yulong Tu ◽  
Liwei Xiong
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Support Vector Machine ◽  
Sustainable Development ◽  
Environmental Conditions ◽  
Metal Content ◽  
Chromium Content ◽  
Principal Component ◽  
Support Vector ◽  
Farmland Soil

Chromium is not only an essential trace element for the growth and development of living organisms; it is also a heavy metal pollutant. Excessive chromium in farmland soil will not only cause harm to crops, but could also constitute a serious threat to human health through the cumulative effect of the food chain. The determination of heavy metals in tailings of farmland soil is an essential means of soil environmental protection and sustainable development. Hyperspectral remote sensing technology has good characteristics, e.g., high speed, macro, and high resolution, etc., and has gradually become a focus of research to determine heavy metal content in soil. However, due to the spectral variation caused by different environmental conditions, the direct application of the indoor spectrum to conduct field surveys is not effective. Soil components are complex, and the effect of linear regression of heavy metal content is not satisfactory. This study builds indoor and outdoor spectral conversion models to eliminate soil spectral differences caused by environmental conditions. Considering the complex effects of soil composition, we introduce a support vector machine model to retrieve chromium content that has advantages in solving problems such as small samples, non-linearity, and a large number of dimensions. Taking a mining area in Hunan, China as a test area, this study retrieved the chromium content in the soil using 12 combination models of three types of spectra (field spectrum, lab spectrum, and direct standardization (DS) spectrum), two regression methods (stepwise regression and support vector machine regression), and two factors (strong correlation factor and principal component factor). The results show that: (1) As far as the spectral types are concerned, the inversion accuracy of each combination of the field spectrum is generally lower than the accuracy of the corresponding combination of other spectral types, indicating that field environmental interference affects the modeling accuracy. Each combination of DS spectra has higher inversion accuracy than the corresponding combination of field spectra, indicating that DS spectra have a certain effect in eliminating soil spectral differences caused by environmental conditions. (2) The inversion accuracy of each spectrum type of SVR_SC (Support Vector Regression_Strong Correlation) is the highest for the combination of regression method and inversion factor. This indicates the feasibility and superiority of inversion of heavy metals in soil by a support vector machine. However, the inversion accuracy of each spectrum type of SVR_PC (Support Vector Regression_Principal Component) is generally lower than that of other combinations, which indicates that, to obtain superior inversion performance of SVR, the selection of characteristic factors is very important. (3) Through principal component regression analysis, it is found that the pre-processed spectrum is more stable for the inversion of Cr concentration. The regression coefficients of the three types of differential spectra are roughly the same. The five statistically significant characteristic bands are mostly around 384–458 nm, 959–993 nm, 1373–1448 nm, 1970–2014 nm, and 2325–2400 nm. The research results provide a useful reference for the large-scale normalization monitoring of chromium-contaminated soil. They also provide theoretical and technical support for soil environmental protection and sustainable development.

scholarly journals Application of Phytoremediation in Restoring Sustainable Development to the Environment: Economic and Soil Conditions

2012 ◽  
Vol 15 (3) ◽  
pp. 37-55 ◽  
Author(s):  
Wiktor Pszczółkowski ◽  
Zdzisława Romanowska-Duda ◽  
Agata Pszczółkowska ◽  
Mieczysław Grzesik ◽  
Zofia Wysokińska
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Sustainable Development ◽  
Cost Effectiveness ◽  
Potential Application ◽  
Metal Content ◽  
Point Of View ◽  
Soil Conditions ◽  
The Cost

The objective of this article is a presentation of priority questions and relations involving economic and soil conditions for the application of phytoremediation technology in restoring sustainable development to the environment. The analysis looks at the justifiability of the application of phytoremediation in restoring a balanced environment as an alternative method to costly land recultivation aimed at eliminating pollutants—a solution that is impossible in the case of large areas. The cost effectiveness of the use of phytoremediation in the recovery of trace element in the soil through the process of phytoremediation was demonstrated. The quality of soils as found in the Voivodeship of Łódź was analyzed from the point of view of potential application of the phytoremediation method, taking into account subdivision by heavy metals found in the soils as well as their origins and properties. Grades of soil purity are presented and border values of heavy metal content were identified.  

scholarly journals Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

2019 ◽  
Vol 6 (5) ◽  
pp. 190001 ◽  
Author(s):  
Katherine E. Klug ◽  
Christian M. Jennings ◽  
Nicholas Lytal ◽  
Lingling An ◽  
Jeong-Yeol Yoon
Keyword(s):  
Heavy Metal ◽  
Support Vector Machine ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Mie Scattering ◽  
Training Data ◽  
Scattering Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Analysis

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

2021 ◽  
Author(s):  
Tim Brandes ◽  
Stefano Scarso ◽  
Christian Koch ◽  
Stephan Staudacher
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Principal Component ◽  
General Term ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Model ◽  
Precision Error ◽  
Operational Conditions ◽  
Wide Range

Abstract A numerical experiment of intentionally reduced complexity is used to demonstrate a method to classify flight missions in terms of the operational severity experienced by the engines. In this proof of concept, the general term of severity is limited to the erosion of the core flow compressor blade and vane leading edges. A Monte Carlo simulation of varying operational conditions generates a required database of 10000 flight missions. Each flight is sampled at a rate of 1 Hz. Eleven measurable or synthesizable physical parameters are deemed to be relevant for the problem. They are reduced to seven universal non-dimensional groups which are averaged for each flight. The application of principal component analysis allows a further reduction to three principal components. They are used to run a support-vector machine model in order to classify the flights. A linear kernel function is chosen for the support-vector machine due to its low computation time compared to other functions. The robustness of the classification approach against measurement precision error is evaluated. In addition, a minimum number of flights required for training and a sensible number of severity classes are documented. Furthermore, the importance to train the algorithms on a sufficiently wide range of operations is presented.

scholarly journals Studi Penurunan Kadar Logam Besi (Fe) dan Logam Tembaga (Cu) pada Air Embung Menggunakan Adsorben Nanosilika

2017 ◽  
Vol 17 (2) ◽  
pp. 131
Author(s):  
Emas Agus Prastyo Wibowo ◽  
Ika Sri Hardyanti ◽  
Isni Nurani ◽  
Dyan Septyaningsih Hardjono HP ◽  
Aden Dhana Rizkita
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Atomic Absorption ◽  
Water Use ◽  
Metal Content ◽  
Atomic Absorption Spectrophotometer ◽  
Absorption Method ◽  
Wastewater Purification ◽  
Stirring Time ◽  
Independent Variable

STUDI PENURUNAN KADAR LOGAM BESI (Fe) DAN LOGAM TEMBAGA (Cu) PADA AIR EMBUNG MENGGUNAKAN ADSORBEN NANOSILIKAABSTRAKPolusi limbah logam berat dalam air merupakan satu permasalahan lingkungan yang penting. Dalam mengatasi permasalahan tersebut dapat dilakukan purifikasi terhadap air tersebut. Metode yang dapat digunakan untuk purifikasi limbah sangat beragam salah satunya adalah absorpsi. Secara umum metode absorpsi telah banyak digunakan dalam purifikasi air limbah. Metode absorpsi dapat menurunkan kadar logam yang terlarut pada limbah. cair dengan cara menyerap logam-logam tersebut ke dalam permukaan absorbennya. Tujuan dilakukan penelitian ini adalah  untuk menurunkan konsentrasi logam besi (Fe) dan tembaga (Cu) menggunakan adsorben nanosilika. Penelitian ini menggunakan variabel bebas yaitu waktu pengadukan (20 menit, 40 menit, dan 60 menit). Hasil akhir filtrat air embung kemudian diukur absorbansinya menggunakan Spektrofotometer Serapan Atom. Berdasarkan hasil analisa menggunakan instrumen SSA diperoleh hasil bahwa tidak terjadi penurunan logam Fe maupun Cu. Dalam hal ini terjadi peningkatan konsentrasi dalam logam Fe maupun Cu, hal ini dikarenakan kurangnya waktu pengadukan dan pengaruh dari adsorben nanosilika.Kata Kunci: limbah, logam berat, nanosilika STUDY OF DECREASING METALS IRON (Fe) AND COPPER (Cu) ON EMBUNG WATER USE OF NANOSILICA ADSORBEN ABSTRACTHeavy waste pollution of heavy metals in the water is an important environment issue. To solve the problem, its can be purified the water. The methods that can be used for waste purification are very diverse, one of which is absorption. In general, the method of absorption has been widely used in wastewater purification. The absorption method can decrease dissolved metal content in the waste. liquid by absorbing the metals into the absorbent surface. Research has been conducted to reduce the concentration of iron (Fe) and copper (Cu) by using nanosilica adsorbent. This research used to independent variable that is stirring time (20 minutes, 40 minutes, and 60 minutes). The final result of filtrate embung water then measured its absorbance using Atomic Absorption Spectrophotometer (AAS). Based on the result of the analysis using SSA instrument, it is found that there is no decrease of Fe and Cu metals. There are several reasons for those problem such as due to lack of stirring time and the influence of nanosilica adsorbent.Keywords: Waste pollution, heavy metal, nanosilica

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