scholarly journals Research on Hyperspectral Inversion of Soil Alkaline Hydrolysis Nitrogen Content and pH Value Based on DWD

2021 ◽  
Vol 2079 (1) ◽  
pp. 012021
Author(s):  
Sijia Liu ◽  
Zhiyun Xiao

Abstract Aiming at the shortcomings of traditional methods for detecting the content of Alkaline Hydrolysis Nitrogen (AHN) and pH value in soil, such as time-consuming and labor-consuming, this paper proposes a rapid quantitative inversion method based on hyperspectral analysis of AHN content and pH value. This method uses db4 discrete wavelet denoising (DWD) and wavelet denoising normalization (DWD-N) to carry out Pearson correlation analysis, and two methods, Ridge regression and Partial Least Squares Regression (PLSR), were used to compare the accuracy of hyperspectral inversion of soil AHN content and pH value. Experiments have demonstrated that in the inversion of the AHN content prediction model, Ridge regression has a good modeling effect under the DWD-N model, where R2=0.647, RMSE=7.067mg/kg. PLSR has good prediction effect under DWD-N, where R2 is the highest of 0.792, RMSE is 3.438mg/kg; in the model inversion of pH prediction, the full-band PLSR modeling effect of pH value under DWD pretreatment is the best, which modeling set and the prediction set of R2 is 0.826 and 0.875, the RMSE is 0.217 mg/kg and 0.191 mg/kg respectively.

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3978
Author(s):  
Rocco Peter Fornari ◽  
Piotr de Silva

Discovering new materials for energy storage requires reliable and efficient protocols for predicting key properties of unknown compounds. In the context of the search for new organic electrolytes for redox flow batteries, we present and validate a robust procedure to calculate the redox potentials of organic molecules at any pH value, using widely available quantum chemistry and cheminformatics methods. Using a consistent experimental data set for validation, we explore and compare a few different methods for calculating reaction free energies, the treatment of solvation, and the effect of pH on redox potentials. We find that the B3LYP hybrid functional with the COSMO solvation method, in conjunction with thermal contributions evaluated from BLYP gas-phase harmonic frequencies, yields a good prediction of pH = 0 redox potentials at a moderate computational cost. To predict how the potentials are affected by pH, we propose an improved version of the Alberty-Legendre transform that allows the construction of a more realistic Pourbaix diagram by taking into account how the protonation state changes with pH.


2020 ◽  
Vol 12 (13) ◽  
pp. 2123 ◽  
Author(s):  
Leran Han ◽  
Chunmei Wang ◽  
Tao Yu ◽  
Xingfa Gu ◽  
Qiyue Liu

This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.


2021 ◽  
Author(s):  
Chaojie Niu ◽  
Xiang Li ◽  
Chengshuai Liu ◽  
Shan-e-hyder Soomro ◽  
Caihong Hu

Abstract Daily reference evapotranspiration (ET0) is the most crucial link in estimating crop water demand. In this study, Levenberg-Marquardt (L-M), Genetic Algorithm-Back Propagation (GA-BP) and Partial Least Squares Regression (PLSR) models were introduced to calculate the ET0 values, Based on the Pearson Correlation analysis method, five meteorological factors were obtained, which were combined into six different input scenarios. Compared with the values that calculated by the the Penman Monteith (PM) formula. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) were used to evaluate the simulation performance of the models. The results showed that the simulation effect of the L-M model is better than that of the GA-BP model and PLSR model in all scenarios. PLSR model has the worst performance. The SI index of L-M6 was 46.69% lower than that of GA-BP6 and 65.78% lower than that of PLSR6. When the input factors are 3, the simulation effect of the input wind speed, the maximum temperature and the minimum temperature is the best. L-M model and GA-BP model can predict the ET0 in the region with a lack of meteorological data. This study provides an important reference for high-precision prediction of ET0 under different input combinations of meteorological factors.


2018 ◽  
Vol 10 (12) ◽  
pp. 4805 ◽  
Author(s):  
Qi-Peng Zhang ◽  
Jian Wang ◽  
Hong-Liang Gu ◽  
Zhi-Gang Zhang ◽  
Qian Wang

Many studies reported the effect on plant functional groups and plant diversity under discontinuous slope gradient. However, studies on the effect of continuous slope gradient on plant functional groups and plant diversity in alpine meadows have rarely been conducted. We studied the effect of a continuous slope gradient on the dominance characteristics of plant functional groups and plant diversity of alpine meadows on the Tibetan plateau—in Hezuo area of Gannan Tibetan Autonomous Prefecture. Altogether, 84 samples of alpine meadows grass and 84 soil samples from seven slope gradients at sunlit slopes were collected. By using analysis of variance (ANOVA) and Pearson correlation coefficient, this study revealed: (1) Continuous slope gradient is an important factor affecting plant communities in alpine meadows, due to the physical and chemical characteristics of the soil and water content. The number of families, genera, and species increased first then decreased at the different slope gradient levels, respectively; (2) there is a close relationship between the soil and plant functional groups, and plant diversity. In other words, the slope determines the functional groups of plants and the soil nutrients; and (3) soil characteristics (pH value, Soil Total Nitrogen, Soil Water Content) are the determining factors of the plant community characteristics at each slope gradient level. To conclude, a continuous slope gradient is an important factor that affects plant communities in alpine meadows.


Author(s):  
Mei-Yin Kuan ◽  
Jiun-Hao Wang ◽  
Yu-Chang Liou ◽  
Li-Pei Peng

Most of the studies on subjective well-being have focused on positive emotions. The adverse effect of negative emotions on mental health has been overlooked. This study investigates the extent to which specific life perceptions are associated with emotional profiles, and explores relevant factors that effectively enhance subjective well-being. The data were drawn from 4656 respondents in the 2015 National Well-being Indicators Survey in Taiwan. T-test, ANOVA, Pearson correlation, and ordinary least squares regression were conducted. The results reveal that perceptions of all life domains are positively associated with life satisfaction and happiness. Depression and worry are negatively associated with most of the life perceptions, except for environmental quality. These results demonstrate that the emotional profile approach sheds light on current literature on subjective well-being, and suggests that strategies to increase well-being should take positive and negative emotion into account simultaneously. The findings contribute by confirming which life domains can produce the best or worst outcomes in emotional regulation and positively influence mental health. Given that personal safety and the future security of external types is the most crucial factor within the emotional profiles, social welfare and protection programs would be an important strategy to increase subjective well-being.


2020 ◽  
Vol 12 (4) ◽  
pp. 1476 ◽  
Author(s):  
Lei Han ◽  
Rui Chen ◽  
Huili Zhu ◽  
Yonghua Zhao ◽  
Zhao Liu ◽  
...  

Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation; R v 2 is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring.


Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1065 ◽  
Author(s):  
Huihui Zhang ◽  
Wenqing Shao ◽  
Shanshan Qiu ◽  
Jun Wang ◽  
Zhenbo Wei

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R2 ≥ 0.9998).


2014 ◽  
Vol 513-517 ◽  
pp. 319-322
Author(s):  
Xiao Li Yang ◽  
Neng Bang Hou ◽  
Jun You Shi ◽  
Yan Fang Li

We studied moisture determination in lignitic coal samples through near-infrared (NIR) technique. This research was developed by applying partical least squares regression (PLS) and discrete wavelet transform (DWT). Firstly, the NIR spectra were pre-processed by DWT for fitting and compression. Then, the compressed data were used to build regression model with PLS for moisture determination in coal samples. Three type DWTs were investigated.Determination performance at different resolution scales was studied. The results show that DWT is a very efficient pre-processing method for NIR spectra analysis.


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