Volume Water Content Prediction of Variable Bulk Density Soil Based on Spectral Characteristics

2013 ◽  
Vol 321-324 ◽  
pp. 309-313
Author(s):  
Zhen Chen ◽  
Lan Cai
Keyword(s):  
Support Vector Machine ◽  
Water Content ◽  
Bulk Density ◽  
Near Infrared ◽  
Spectral Characteristics ◽  
Characteristic Vector ◽  
Support Vector ◽  
Model Parameters ◽  
Undisturbed Soil ◽  
Soil Volume

In order to eliminate effects of soil bulk density variance and soil properties difference on soil water content forecast precision, a method was proposed to forecast soil volume water content based on near-infrared spectroscopy. This paper investigated relationship between volume water content and near infrared spectral reflectance characteristics in 900-2500nm band, used spectral parameters and support vector machine to built quantitative prediction model for three type variable bulk density soil volume water content, normalized signal characteristics by relative dry soil characteristic vector, putted forward three further processing methods. It was used support vector machine (SVM) method to establish spectral characteristics inverting soil volume water content model of undisturbed soil, and model parameters were optimized by genetic algorithm, through predict error comparison, the final determination was that relative characteristics variation of first-order derivative signal as model input characteristic vector, GA-SVM model prediction had best effect and its forecast error was 1.7866.

scholarly journals Short-term power load forecasting based on support vector machine and particle swarm optimization

2018 ◽  
Vol 13 ◽  
pp. 174830181879706 ◽  
Author(s):  
Song Qiang ◽  
Yang Pu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Influencing Factors ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Model Parameters ◽  
Short Term ◽  
Swarm Optimization

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the “abnormal data” in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.

scholarly journals Soft Measurement of Water Content in Oil-Water Two-Phase Flow Based on RS-SVM Classifier and GA-NN Predictor

2014 ◽  
Vol 14 (4) ◽  
pp. 219-226 ◽  
Author(s):  
Dongzhi Zhang ◽  
Bokai Xia
Keyword(s):  
Water Content ◽  
Measurement Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Model Parameters ◽  
Water Mixture ◽  
Measuring Range ◽  
Two Phase ◽  
Soft Measurement ◽  
Oil Water

Abstract Measurement of water content in oil-water mixing flow was restricted by special problems such as narrow measuring range and low accuracy. A simulated multi-sensor measurement system in the laboratory was established, and the influence of multi-factor such as temperature, and salinity content on the measurement was investigated by numerical simulation combined with experimental test. A soft measurement model based on rough set-support vector machine (RS-SVM) classifier and genetic algorithm-neural network (GA-NN) predictors was reported in this paper. Investigation results indicate that RS-SVM classifier effectively realized the pattern identification for water holdup states via fuzzy reasoning and self-learning, and GA-NN predictors are capable of subsection forecasting water content in the different water holdup patterns, as well as adjusting the model parameters adaptively in terms of online measuring range. Compared with the actual laboratory analyzed results, the soft model proposed can be effectively used for estimating the water content in oil-water mixture in all-round measuring range

scholarly journals Food powders classification using handheld Near-Infrared Spectroscopy and Support Vector Machine

2019 ◽  
Vol 1367 ◽  
pp. 012029 ◽  
Author(s):  
Mohamed Yasser Mohamed ◽  
Mahmud Iwan Solihin ◽  
Winda Astuti ◽  
Chun Kit Ang ◽  
Wan Zailah
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Support Vector ◽  
Food Powders

A mathematical model for variation in water-retention curves among sandy soils

2007 ◽  
Vol 19 (4) ◽  
pp. 427-436 ◽  
Author(s):  
H.W. Hunt ◽  
A.M. Treonis ◽  
D.H. Wall ◽  
R.A. Virginia
Keyword(s):  
Particle Size ◽  
Water Content ◽  
Bulk Density ◽  
Water Retention ◽  
Organic Matter Content ◽  
Sandy Soils ◽  
Sand Particle ◽  
Model Parameters ◽  
Calibration Data ◽  
Matric Potential

AbstractEquations were developed to predict soil matric potential as a function of soil water content, texture and bulk density in sandy soils. The equations were based on the additivity hypothesis - that water-retention of a whole soil depends on the proportions of several particle size fractions, each with fixed water-retention characteristics. The new model is an advancement over previously published models in that it embodies three basic properties of water-retention curves: a) matric potential is zero at saturation water content, b) matric potential approaches -∞ as water content approaches zero, and c) volumetric water content in dry soil is proportional to bulk density. Values of model parameters were taken from the literature, or estimated by fitting model predictions to data for sandy soils with low organic matter content. Most of the variation in water-release curves in the calibration data was explained by texture, with negligible effects of bulk density and sand particle size. The model predicted that variation in clay content among soils within the sand and loamy sand textural classes had substantial effects on water-retention curves. An understanding of how variation in texture among sandy soils contributes to matric potential is necessary for interpreting biological activity in arid environments.

scholarly journals A New Plant Indicator (Artemisia lavandulaefolia DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Lu Xu ◽  
Qiong Shi ◽  
Bang-Cheng Tang ◽  
Shunping Xie
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Least Squares ◽  
Near Infrared ◽  
Mercury Level ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Classification Model ◽  
Support Vector ◽  
Reference Analysis

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

scholarly journals Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1611 ◽  
Author(s):  
Hwan-Hui Lim ◽  
Enok Cheon ◽  
Deuk-Hwan Lee ◽  
Jun-Seo Jeon ◽  
Seung-Rae Lee
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Near Infrared ◽  
Classification Model ◽  
Projection Algorithm ◽  
Support Vector ◽  
Ann Model ◽  
Landslide Hazards ◽  
Combining Data

Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.

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