Factors affecting the soil–water retention curve of Chinese loess

Zhang, Fanyu; Zhao, Chongxi; Lourenço, Sérgio D. N.; Dong, Simeng; Jiang, Yao

doi:10.1007/s10064-020-01959-9

Cited by 29 publications

(7 citation statements)

References 53 publications

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“…Most recent research indicates the importance of determining SWCC from data in the field and in the laboratory [32][33][34][35]. The shape and format of SWCC depends on soil's physical and hydraulic parameters and has an impact on water behavior and motion [36][37][38]. Moreover, some scientists have turned to new techniques, such as machine learning and artificial intelligence, with promising results [39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Hydraulic Parameters from the Soil Water Characteristic Curve

2023

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Soil water characteristic curve (SWCC) is one of the most essential hydraulic properties that play fundamental role in various environmental issues and water management. SWCC gives important information for water movement, soil behavior, infiltration, and drainage mechanism, affecting the water circle and the aquifer recharge. Since most of the world’s freshwater withdrawals go for irrigation uses, decoding SWCC is beneficial, as it affects water saving through irrigation planning. Estimation of crucial parameters, such as field capacity (FC) and permanent wilting point (PWP) is the key solution for water saving. Modelling of the SWCC and hydraulic parameters estimation are of great importance, since the laboratory experimental procedures and the experiments in the field are often time-consuming processes. In the present study, the SWCC along with FC and PWP of two soil types were obtained via specific experimental procedures in the laboratory. In order to simulate the SWCC and estimate FC and PWP, the experimental data were approximated with van Genuchten’s model. Results showed that using SWCC to estimate FC gives excellent results, while the method rationally overestimates the PWP. Hence, the presented method leads to estimation of crucial hydraulic parameters that can be used in irrigation planning and water saving practices.

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Section: Introductionmentioning

confidence: 99%

Estimation of Hydraulic Parameters from the Soil Water Characteristic Curve

2023

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“…Soil texture (Liu et al 2012 ) and pore conformations (Chen et al 2017 ) have been found to pose a significant influence on total SWCs. Moreover, the initial soil wetting has also been investigated to impact the water content at large (Zhang et al 2021 ). However, the rapid draining of water due to gravitational pull generates a sufficient number of atrocities in data collection and further analytical processing.…”

Section: Introductionmentioning

confidence: 99%

AI and machine learning for soil analysis: an assessment of sustainable agricultural practices

Awais,

Naqvi,

Zhang

et al. 2023

Bioresour. Bioprocess.

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Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil–water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher’s insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database. Graphical Abstract

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“…In addition, loess has very complex soil-water and engineering characteristics in an unsaturated state [10]. In general, most unsaturated slopes tend to lose stability during the process of seepage or when water pressure rises in soil pores [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the PSD parameters b and c can be used to express the SWCC parameters a and n by means of Equations (8)-(10). a = −1.701l + 54.969 (8) b = 26.56m − 31.41 (9)c = −0.492n + 2.543(10)…”

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confidence: 99%

The Influence of Drying-Wetting Cycles on the Suction Stress of Compacted Loess and the Associated Microscopic Mechanism

Nie

et al. 2021

Water

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To better understand and analyze the unsaturated stability of loess filling body, it is necessary to study the changes in suction stress before and after the drying-wetting cycles. In this study, the SWCC of compacted loess before and after drying-wetting cycles was tested using the filter paper method. Then, the suction stress was calculated and the microstructure of the loess sample was determined by the SEM and NMR. The results showed that the drying-wetting cycles had an important influence on the SSCC and microstructure of compacted loess. The change in suction stress before and after the drying-wetting cycles can be well explained by the loess microstructure. The drying-wetting cycles did not significantly change the basic trend of the compacted loess’s SSCC, but it increased the porosity and the dominant pore diameter of loess, and reduced the suction stress under the same matric suction. The main significant change in suction stress with matric suction occurred within the range of the dominant soil pores. The larger the dominant pore diameter, the smaller the suction stress under the same matric suction. In addition, this study proposes a new method for calculating suction stress based on the PSD parameters.

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Factors affecting the soil–water retention curve of Chinese loess

Cited by 29 publications

References 53 publications

Estimation of Hydraulic Parameters from the Soil Water Characteristic Curve

Estimation of Hydraulic Parameters from the Soil Water Characteristic Curve

AI and machine learning for soil analysis: an assessment of sustainable agricultural practices

The Influence of Drying-Wetting Cycles on the Suction Stress of Compacted Loess and the Associated Microscopic Mechanism

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