Dynamics of soil organic carbon and nitrogen and their relations to hydrothermal variability in dryland

He, Mingzhu; Tang, Liang; Li, Chengyi; Ren, Jianxin; Zhang, Libin; Li, Xinrong

doi:10.1016/j.jenvman.2022.115751

Cited by 7 publications

(3 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, the Adaboost technique consists of four key steps: (1) data collection, (2) creation of strong learners from base learners, (3) testing and validation of the boosted algorithms, and (4) application of the strong learners to real-world issues. The main levels involved in the boosting process are the integration of weak learners into strong learners and the instruction of weak learners using training data . The base learner parameters and those of the Adaboost framework make up the main Adaboost parameters.…”

Section: Overview Of the Developed Algorithmsmentioning

confidence: 99%

“…The main levels involved in the boosting process are the integration of weak learners into strong learners and the instruction of weak learners using training data. 42 The base learner parameters and those of the Adaboost framework make up the main Adaboost parameters. While the latter considers the number of estimators and the learning rate, the former depends on the employed base learners.…”

Section: Overview Of the Developed Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques

et al. 2023

View full text Add to dashboard Cite

Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance. The knowledge of soil unconfined compressive strength (UCS) enables engineers to identify the behaviors of a particular soil and propose effective solutions to given geotechnical problems. However, the experimental techniques involved in the measurements of soil UCS are incredibly expensive and time-consuming. In this work, we develop a pioneering technique to estimate the soil unconfined compressive strength using artificial intelligent methods based on the spectra obtained from the LIBS system. Decision tree regression (DTR) and support vector regression learners were initially employed, and consequently, the adaptive boosting method was applied to improve the performance of the two single learners. The prediction power of the established models was determined using the standard performance evaluation metrics such as the root-mean-square error, CC between the predicted and actual soil UCS values, mean absolute error, and R 2 score. Our results revealed that the boosted DTR exhibited the highest coefficient of correlation of 99.52% and an R 2 value of 99.03% during the testing phase. To validate the models, the UCS values of soils stabilized with lime and cement were predicted with an optimum degree of accuracy, confirming the models’ suitability and generalization strength for soil UCS investigations.

show abstract

Section: Overview Of the Developed Algorithmsmentioning

confidence: 99%

Section: Overview Of the Developed Algorithmsmentioning

confidence: 99%

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques

et al. 2023

View full text Add to dashboard Cite

show abstract

“…From 1970 to the end of 1990, Alxa League appeared to have a tendency towards the increase in desertification, but the desertification appeared to have a decreasing trend after 2000; currently, the desertification of Alxa League has been inhibited, while the ecological governing area of the Helan mountain area northwest of the Tengger Desert and the Ejina banner in the Heihe River Basin has improved [25,26]. In recent years, an increase in precipitation in Alxa League was found, and it had a positive effect on carbon sequestration [27]. The dynamic changes in the desertification of the Tengger Desert from 1973 to 2009 were determined using five periods of remote sensing data, and the data also pointed out that reasonable and effective human activities in the southern region of the Tengger Desert was playing a crucial role in preventing desertification [28].…”

Section: Introductionmentioning

confidence: 99%

The Latest Desertification Process and Its Driving Force in Alxa League from 2000 to 2020

Xie,

Lu,

Xiao

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Alxa League of Inner Mongolia Autonomous Region is a concentrated desert distribution area in China, and the latest desertification process and its driving mechanism under the comprehensive influence of the extreme dry climate and intense human activities has attracted much attention. Landsat data, including ETM+ images obtained in 2000, TM images obtained in 2010, and OLI images obtained in 2020, were used to extract three periods of desertification land information using the classification and regression tree (CART) decision tree classification method in Alxa League. The spatio-temporal variation characteristics of desertification land were analyzed by combining the transfer matrix and barycenter migration model; the effects of climate change and human activities on regional desertification evolution were separated and recombined using the multiple regression residual analysis method and by considering the influence of non-zonal factors. The results showed that from 2000 to 2020, the overall area of desertification land in Alxa League was reduced, the desertification degree was alleviated, the desertification trend was reversed, and the desertification degree in the northern part of the region was more serious than in the southern part. The barycenter of the slight, moderate, and severe desertification land migrated to the southeast, whereas the serious desertification land’s barycenter migrated to the northwest in the period of 2000–2010; however, all of them hardly moved from 2010 to 2020. The degree of desertification reversal in the south was more significant than in the north. Regional desertification reversal was mainly influenced by the combination of human activities and climate change, and the area accounted for 61.5%; meanwhile, the localized desertification development was mainly affected by human activities and accounted for 76.8%.

show abstract

Analyzing and forecasting climate variability in Nainital district, India using non-parametric methods and ensemble machine learning algorithms

Sharma,

Sajjad,

Saha

et al. 2024

Theor Appl Climatol

View full text Add to dashboard Cite

Dynamics of soil organic carbon and nitrogen and their relations to hydrothermal variability in dryland

Cited by 7 publications

References 65 publications

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques

The Latest Desertification Process and Its Driving Force in Alxa League from 2000 to 2020

Analyzing and forecasting climate variability in Nainital district, India using non-parametric methods and ensemble machine learning algorithms

Contact Info

Product

Resources

About