Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Karray, Emna; Elmannai, Hela; Toumi, Elyes; Gharbia, Mohamed Hedi; Meshoul, Souham; Aïchi, Hamouda; Rabah, Zouhaier Ben

doi:10.32604/cmes.2023.023164

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…Typical values for parameter identification of relevant evaluation standards refer to relevant industry standards. In this paper, the gray prediction model [16], ARIMA model [17], SVR regression model [18], single prediction model and combined prediction model are selected for a comparative analysis to evaluate the predictive performance of the model.…”

Section: Methodsmentioning

confidence: 99%

Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China

Liu

Wang

et al. 2023

Energies

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China aims to reduce carbon dioxide emissions and achieve peak carbon and carbon neutrality goals. Natural gas, as a high-quality fossil fuel energy, is an important transition resource for China in the process of carbon reduction, so it is necessary to predict China’s natural gas demand. In this paper, a novel natural gas demand combination forecasting model is constructed to accurately predict the future natural gas demand. The Lasso model and the polynomial model are used to build a combinatorial model, which overcomes the shortcomings of traditional models, which have low data dimensions and poor prediction abilities. In the modeling process, the cross-validation method is used to adjust the modeling parameters. By comparing the performance of the combinatorial forecasting model, the single forecasting model and other commonly used forecasting models, the results show that the error (2.99%) of the combinatorial forecasting model is the smallest, which verifies the high accuracy and good stability advantages of the combinatorial forecasting model. Finally, the paper analyzes the relevant data from 1999 to 2022 and predicts China’s natural gas demand in the next 10 years. The results show that the annual growth rate of China’s natural gas demand in the next 10 years will reach 13.33%, at 8.3 × 1011 m3 in 2033, which proves that China urgently needs to rapidly develop the gas supply capacity of gas supply enterprises. This study integrates the impact of multiple factors on the natural gas demand, predicts China’s natural gas demand from 2023 to 2033, and provides decision-making support for China’s energy structure adjustment and natural gas import trade.

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Section: Methodsmentioning

confidence: 99%

Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China

Liu

Wang

et al. 2023

Energies

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“…The model primarily relies on support vectors (i.e., data points near the boundary) rather than all data, making it less sensitive to outliers [27]. SVR can effectively handle data in high-dimensional feature spaces, working well even when the number of features exceeds the number of samples [28].…”

Section: Locust Density Inversion Modelmentioning

confidence: 99%

Remote Sensing Monitoring of Grassland Locust Density Based on Machine Learning

Du,

Wang,

Huang

et al. 2024

Sensors

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The main aim of this study was to utilize remote sensing data to establish regression models through machine learning to predict locust density in the upcoming year. First, a dataset for monitoring grassland locust density was constructed based on meteorological data and multi-source remote sensing data in the study area. Subsequently, an SVR (support vector regression) model, BP neural network regression model, random forest regression model, BP neural network regression model with the PCA (principal component analysis), and deep belief network regression model were built on the dataset. The experimental results show that the random forest regression model had the best prediction performance among the five models. Specifically, the model achieved a coefficient of determination (R2) of 0.9685 and a root mean square error (RMSE) of 1.0144 on the test set, which were the optimal values achieved among all the models tested. Finally, the locust density in the study area for 2023 was predicted and, by comparing the predicted results with actual measured data, it was found that the prediction accuracy was high. This is of great significance for local grassland ecological management, disaster warning, scientific decision-making support, scientific research progress, and sustainable agricultural development.

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“…Additionally, they may cause harmful reagents to have an impact on the environment. For instance, since soil texture and electromagnetic radiation physical characteristics are strongly related, the stretching and bending of N-H, O-H, and C-H are the primary causes of the soil's spectral reflectance absorption characteristics [4][5][6]. The characteristics of different particle compositions within soil texture are thus captured by soil reflectance data, including particle size distribution, scattering, and absorption properties.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area

Pan,

Cai,

Zhao

et al. 2023

Applied Sciences

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Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture’s spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R2) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R2 and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture.

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Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination

Cited by 5 publications

References 56 publications

Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China

Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China

Remote Sensing Monitoring of Grassland Locust Density Based on Machine Learning

Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area

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