scholarly journals Effect of projected climate change on potential evapotranspiration in the semiarid region of central India

2020 ◽  
Author(s):  
Darshana Duhan ◽  
Dharmendra Singh ◽  
Sandeep Arya
Keyword(s):  
Potential Evapotranspiration ◽  
Central India ◽  
Least Square ◽  
Cmip5 Models ◽  
Semiarid Region ◽  
Support Vector ◽  
Pan Evaporation ◽  
Empirical Formulas ◽  
Svm Model ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract The performance of potential evapotranspiration (PET) methods such as pan evaporation (physical measurement), empirical formulas (Penman–Monteith (PM), Hargreaves and Thornthwaite) and satellite-derived PET (MOD16) were assessed in a semiarid region of central India. The satellite-based PET was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS). The comparisons between different methods were made with observed pan evaporation (ETpan) data to identify the best method for the semiarid region. Further, the future projection of PET was carried out using RCP4.5 emission scenario of seven CMIP5 models. Two approaches were applied for the projection of PET. In the first approach, RCP4.5 scenario data are directly used in the PM method, and in the second approach, these variables are used as a predictor in the calibrated and validated least square support vector machine (LS-SVM) model. The projection of PET was made using the best-identified model among PM and LS-SVM from the years 2006–2100. The results show that MOD16 and Hargreaves overestimate the PET; however, PM and Thornwaite underestimate the PET. PM based PET is closely related with ETpan and is a good indicator of ETpan in a semiarid region. GFDL-ESM2M is identified as the most skillful CMIP5 model, and results show that PET is projected to increase in future using the LS-SVM model.

Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

2011 ◽  
Vol 460-461 ◽  
pp. 667-672
Author(s):  
Yun Zhao ◽  
Xing Xu ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

scholarly journals Surface roughness estimation for FDM systems

2021 ◽  
Author(s):  
Behnam Nourghassemi
Keyword(s):  
Surface Roughness ◽  
Hybrid Model ◽  
Least Square ◽  
Experimental Results ◽  
Analytical Models ◽  
Support Vector ◽  
Empirical Estimation ◽  
Standard Format ◽  
Svm Model ◽  
Analytical Estimation

By selecting the optimal build angle, the surface roughness of rapid prototyped parts can be minimized. The objective of this study is to develop a model for estimation of surface roughness as a function of build angle and other build parameters for parts built by Fusion Deposition Modeling (FDM) machines. For that purpose, principles of the FDM technique, along with other rapid prototyping techniques, are reviewed and various standards for surface roughness measurements are introduced. Different analytical models for the estimation of surface roughness for FDM systems, which were proposed in the literature, are reviewed and reformulated in a standard format for comparison reasons. A new hybrid model is proposed for analytical estimation of the surface roughness based on experimental results and comparison of the models. In addition, Least Square Support Vector Machine (LS-SVM) is applied for an empirical estimation of the surface roughness. Robustness of the LS-SVM model is studied and its performance is compared to the hybrid model. The experimental results confirm better results for the LS-SVM model.

scholarly journals Surface roughness estimation for FDM systems

2021 ◽  
Author(s):  
Behnam Nourghassemi
Keyword(s):  
Surface Roughness ◽  
Hybrid Model ◽  
Least Square ◽  
Experimental Results ◽  
Analytical Models ◽  
Support Vector ◽  
Empirical Estimation ◽  
Standard Format ◽  
Svm Model ◽  
Analytical Estimation

By selecting the optimal build angle, the surface roughness of rapid prototyped parts can be minimized. The objective of this study is to develop a model for estimation of surface roughness as a function of build angle and other build parameters for parts built by Fusion Deposition Modeling (FDM) machines. For that purpose, principles of the FDM technique, along with other rapid prototyping techniques, are reviewed and various standards for surface roughness measurements are introduced. Different analytical models for the estimation of surface roughness for FDM systems, which were proposed in the literature, are reviewed and reformulated in a standard format for comparison reasons. A new hybrid model is proposed for analytical estimation of the surface roughness based on experimental results and comparison of the models. In addition, Least Square Support Vector Machine (LS-SVM) is applied for an empirical estimation of the surface roughness. Robustness of the LS-SVM model is studied and its performance is compared to the hybrid model. The experimental results confirm better results for the LS-SVM model.

Nondestructive Determination of Citric Acid Using Successive Projections Algorithm and Spectroscopic Techniques

2011 ◽  
Vol 460-461 ◽  
pp. 9-14
Author(s):  
Fei Liu ◽  
Yong He
Keyword(s):  
Citric Acid ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Square ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Optimal Prediction ◽  
Successive Projections Algorithm ◽  
Svm Model ◽  
Successive Projections

Successive projections algorithm (SPA) combined with least square-support vector machine (LS-SVM) was investigated to determine the citric acid of lemon vinegar by 13 wavelengths within visible/near infrared (Vis/NIR) spectral region. Five concentration levels (100%, 80%, 60%, 40% and 20%) of lemon vinegar were prepared, and the calibration set consisted of 150 samples, validation set consisted of 75 samples and the remaining 75 samples for prediction set. After the comparison of different preprocessing such as smoothing, standard normal variate and derivative, SPA was applied to extract the effective wavelengths to reduce the redundancies and collinearity of variables, and the multiple linear regression (MLR) models were developed compared with partial least squares (PLS) models. Simultaneously, the selected wavelengths were used as the inputs of LS-SVM, and a new proposed combination of SPA-LS-SVM model was developed. The results indicated that SPA-LS-SVM achieved the optimal prediction performance, and the correlation coefficient (r) and root mean square error of prediction (RMSEP) were 0.9894 and 0.0623, respectively. An excellent prediction precision was obtained. The overall results demonstrated that it was feasible to utilize Vis/NIR spectroscopy to predict the citric acid of lemon vinegar, and SPA-LS-SVM model achieved the optimal prediction precision. This study supplied a feasible method for the process monitoring of fruit vinegar manufacture and fermentation.

Outlier Detection for Observational Data of Automatic Meteorological Station Based on Least Square Support Vector Machine

2013 ◽  
Vol 295-298 ◽  
pp. 945-949
Author(s):  
Zhen Meng ◽  
Shi Chang Zhang ◽  
Zeng Lin Huang
Keyword(s):  
Support Vector Machine ◽  
Observational Data ◽  
Outlier Detection ◽  
Meteorological Data ◽  
Meteorological Factor ◽  
Meteorological Station ◽  
Temperature Data ◽  
Least Square ◽  
Support Vector ◽  
Svm Model

Automatic meteorological station is important for meteorological observation and the existence of outliers in the observational data is inevitable. The paper proposes outlier detection for observational data of automatic meteorological station based on least square support vector machine (LS-SVM). The method establishes the LS-SVM model for the meteorological factor and uses the model to evaluate the observational data. If the observational data deviate from the model, they would be seemed as outliers. The ground temperature data observed by two real automatic meteorological stations are used in experiments. Experiments results verify that the proposed method realize outlier detection for observational data of automatic meteorological station effectively and ensures subsequent process and analysis of the meteorological data.

Spectroscopy quantitative analysis cotton content of blend fabrics

2016 ◽  
Vol 28 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Xudong Sun ◽  
Mingxing Zhou ◽  
Yize Sun
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Svm Model ◽  
Calibration Methods ◽  
Learning Machine ◽  
Mean Square Errors

Purpose – The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics. Design/methodology/approach – In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively. Findings – The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics. Originality/value – It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

scholarly journals Edible Gelatin Diagnosis Using Laser-Induced Breakdown Spectroscopy and Partial Least Square Assisted Support Vector Machine

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4225 ◽  
Author(s):  
Hao Zhang ◽  
Shun Wang ◽  
Dongxian Li ◽  
Yanyan Zhang ◽  
Jiandong Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Partial Least Square ◽  
Least Square ◽  
Computational Time ◽  
Laser Induced Breakdown Spectroscopy ◽  
Accurate Estimation ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Svm Model ◽  
Laser Induced Breakdown

Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square–support vector machine (PLS-SVM) method for the fast and accurate estimation of edible gelatin adulteration. Gelatin samples with 11 different adulteration ratios were prepared by mixing pure edible gelatin with industrial gelatin, and the LIBS spectra were recorded to analyze their elemental composition differences. The PLS, SVM, and PLS-SVM models were separately built for the prediction of gelatin adulteration ratios, and the hybrid PLS-SVM model yielded a better performance than only the PLS and SVM models. Besides, four different variable selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), random frog (RF), and principal component analysis (PCA), were adopted to combine with the SVM model for comparative study; the results further demonstrated that the PLS-SVM model was superior to the other SVM models. This study reveals that the hybrid PLS-SVM model, with the advantages of low computational time and high prediction accuracy, can be employed as a preferred method for the accurate estimation of edible gelatin adulteration.

Nondestructive Identification of Cherry-Tomato Varieties Based on Multi-Spectral Image Technology

2010 ◽  
Vol 108-111 ◽  
pp. 262-267 ◽  
Author(s):  
Kai Sheng Yang ◽  
Peng Cheng Nie ◽  
Lei Feng ◽  
Yong He ◽  
Shuang Shuang Chen
Keyword(s):  
Discriminant Analysis ◽  
Nutritive Value ◽  
Least Square ◽  
Support Vector ◽  
Cherry Tomato ◽  
Analysis Model ◽  
Spectral Image ◽  
Svm Model ◽  
Nondestructive Identification ◽  
Cherry Tomatoes

Yunnan cherry-tomato and Xinjiang cherry-tomato were very similar in appearance. But they are different in taste and nutritive value. A nondestructive identification method of cherry-tomato variety based on multi-spectral image technology is proposed in this paper. Fifty Yunnan cherry-tomatoes and fifty Xinjiang cherry-tomatoes were selected, and photos were taken by Duncan MS3100 3CCD multi-spectral imager. Threshold based segmentation and mathematical morphology method were used to process the images. Nine characteristic variables were calculated to establish discriminant analysis model (DA) and least square-support vector machine model (LS-SVM). The prediction accuracy of discriminant analysis model was 72.5% and that of LS-SVM model was 80%. The results showed that LS-SVM model could identify Yunnan cherry-tomato and Xinjiang cherry-tomato well.

scholarly journals Estimation of Monthly Total Dissolved Solids Using ANN and LS-SVM Techniques in the Aji Chay River, Iran

2021 ◽  
Vol 10 (1) ◽  
pp. 1-10
Author(s):  
Mahdieh JannatKhah ◽  
Abolghasem Akbari ◽  
Aida Bagheri Basmanji ◽  
Ebrahim Rahmani ◽  
Jonathan Peter Cox
Keyword(s):  
Back Propagation ◽  
Least Square ◽  
Urmia Lake ◽  
Accurate Estimation ◽  
Support Vector ◽  
Ann Model ◽  
North West ◽  
Feed Forward Back Propagation ◽  
Svm Model ◽  
Monthly Total

This research follows on from diverse international efforts to safeguard one of the largest natural lakes in the world, Urmia lake in North West Iran. In this research two new numerical packages based on Artificial Neural Networks (ANN) and the Least Square Support Vector Machine (LS-SVM) models were developed to estimate monthly Total Dissolved Solid (TDS) in the Aji Chay River, one the main tributaries of Urmia lake, Iran. A feed forward back propagation (FFB) model was used to obtain a set of coefficients for a linear model, and the radial basis function (RBF) kernel was employed for the LS-SVM model. The input data sets of both the ANN and LS-SVM models consists of six water quality parameters: TDS, Mg2+, Na+, Ca2+, Cl-, and SO4 2-, all collected on a monthly time scale over a period of 30 years from the Vanyar and Zarnagh stations, in the Aji Chay watershed. The research demonstrated that both models can effectively predict the variability of TDS, but for the Vanyar station with the ANN model (giving an R2 value of 0.913 and RMSE of 0.0032, a Nash-Sutcliffe Efficiency (NSE) coefficient 0.812 and as such has a more efficient and accurate estimation when compared to the LS-SVM model with R2=0.871 and RMSE =0.097 and NSE=0.86. The analysis of Zarnagh station data shows R2=0.853 and RMSE=0.0162, NSE= 0.854 for SVM and R2=0.903 and RMSE =0.0091 and NSE=0.85 for ANN.

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