Stochastic relaxation of nonlinear soil moisture ocean salinity (SMOS) soil moisture retrieval errors with maximal Lyapunov exponent optimization

Lee, Ju Hyoung; Ahn, Choon Ki

doi:10.1007/s11071-018-4588-0

Cited by 8 publications

(5 citation statements)

References 67 publications

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“…Furthermore, kurtosis is typically between −10 (heavytailed) and +10 (light-tailed), which aids in determining the form of a probability distribution, as explained by Brown and Greene (Brown and Greene, 2006). The kurtosis values for M w and t are negative and range between −0.3 and −0.1 (follow mesokurtic distribution), whereas the rest are positive values (follow leptokurtic distribution) (Benson, 1993;Lee and Ahn, 2019). Pearson's correlation coefficient (ρ) was used to measure the strength of a linear association between two variables.…”

Section: Sample Library and Correlation Analysismentioning

confidence: 97%

The performance comparison of the decision tree models on the prediction of seismic gravelly soil liquefaction potential based on dynamic penetration test

et al. 2023

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Seismic liquefaction has been reported in sandy soils as well as gravelly soils. Despite sandy soils, a comprehensive case history record is still lacking for developing empirical, semi-empirical, and soft computing models to predict this phenomenon in gravelly soils. This work compiles documentation from 234 case histories of gravelly soil liquefaction from across the world to generate a database, which will then be used to develop seismic gravelly soil liquefaction potential models. The performance measures, namely, accuracy, precision, recall, F-score, and area under the receiver operating characteristic curve, were used to evaluate the training and testing tree-based models’ performance and highlight the capability of the logistic model tree over reduced error pruning tree, random tree and random forest models. The findings of this research can provide theoretical support for researchers in selecting appropriate tree-based models and improving the predictive performance of seismic gravelly soil liquefaction potential.

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Section: Sample Library and Correlation Analysismentioning

confidence: 97%

The performance comparison of the decision tree models on the prediction of seismic gravelly soil liquefaction potential based on dynamic penetration test

et al. 2023

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“…The roughness ensembles generated by Equation ( 4) and the original deterministic Radarsat-2 backscattering were used to produce soil moisture ensembles. The mean values of those ensemble were used as bias-corrected final products [38].…”

Section: Stochastic Bias Correctionmentioning

confidence: 99%

“…Lee et al [34] also mitigated retrieval errors related to rainfall and vegetation by the construction of PDFs of uncertain parameters, such as roughness or SAR backscatter measurements. These stochastic systems can be optimized by clustering analysis [38,39].…”

Section: Introductionmentioning

confidence: 99%

Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at An Agricultural Watershed

Lee

2023

Remote Sensing

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Satellites are designed to monitor geospatial data over large areas at a catchment scale. However, most of satellite validation works are conducted at local point scales with a lack of spatial representativeness. Although upscaling them with a spatial average of several point data collected in the field, it is almost impossible to reorganize backscattering responses at pixel scales. Considering the influence of soil storage on watershed streamflow, we thus suggested watershed-scale hydrological validation. In addition, to overcome the limitations of backscattering models that are widely used for C-band Synthetic Aperture Radar (SAR) soil moisture but applied to bare soils only, in this study, RADARSAT-2 soil moisture was stochastically retrieved to correct vegetation effects arising from agricultural lands. Roughness-corrected soil moisture retrievals were assessed at various spatial scales over the Brightwater Creek basin (land cover: crop lands, gross drainage area: 1540 km2) in Saskatchewan, Canada. At the point scale, local station data showed that the Root Mean Square Errors (RMSEs), Unbiased RMSEs (ubRMSEs) and biases of Radarsat-2 were 0.06~0.09 m3/m3, 0.04~0.08 m3/m3 and 0.01~0.05 m3/m3, respectively, while 1 km Soil Moisture Active Passive (SMAP) showed underestimation at RMSEs of 0.1~0.22 m3/m3 and biases of −0.036~−0.2080 m3/m3. Although SMAP soil moisture better distinguished the contributing area at the catchment scale, Radarsat-2 soil moisture showed a better discharge hysteresis. A reliable estimation of the soil storage dynamics is more important for discharge forecasting than a static classification of contributing and noncontributing areas.

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“…Te bold values represent the result of optimum trials.determines whether the data is light-tailed or heavy-tailed in comparison to a particular normal distribution. It can be categorized as platykurtic (Kurtosis < 3: ρ dmax , w opt , w n , C F ,w L , I P , silt, P s ), mesokurtic (Kurtosis � 3: sand) and leptokurtic distribution (Kurtosis > 3: S p ), which is also displayed and verifed from Figure5[108]; Brown and Greene[109,110].…”

mentioning

confidence: 67%

Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling

Jalal

Iqbal

Khan

et al. 2023

Advances in Materials Science and Engineering

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In this article, detailed trials were undertaken to study the variation in genetic parameters in order to formulate more robust predictive models using gene expression programming (GEP) and multigene expression programming (MEP) for computing the swelling pressure of expansive soils (Ps-ES). A total of 200 datasets with ten input parameters (i.e., clay fraction CF, liquid limit w L , plastic limit w P , plasticity index IP, specific gravity Gs, swell percent Sp, sand content, silt content, maximum dry density ρdmax, and optimum water content w o p t ) and one output variable, i.e., Ps-ES are collected from the literature, which comprises 120 internationally publications. The effect of input parameters in contributing to Ps-ES has been validated using Pearson correlation (r), sensitivity analysis (SA), as well as a parametric study. The results reveal that the GP-based techniques correctly characterize the swelling characteristics of the ES, thus leading to reasonable prediction performance; however, the MEP model yielded relatively better performance. Also, the proposed predictive models were compared with widely used AI models (ANN, ANFIS, RF, GB-T, DT, and SVM). The ANN performed relatively better; however, it is recommended to use the GEP and MEP due to the blackbox nature of the ANN. Other models exhibited inferior performance. The SA revealed different importance by the GEP and MEP models, however, its confirmed that the maximum dry density and optimum moisture content significantly affect the Ps-ES. The variation in Ps-ES with changes in input attributes is further corroborated from literature. Hence, it is recommended that the proposed GEP and MEP models can be deployed for computing the Ps-ES which efficiently lessens the laborious and time-consuming testing.

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Stochastic relaxation of nonlinear soil moisture ocean salinity (SMOS) soil moisture retrieval errors with maximal Lyapunov exponent optimization

Cited by 8 publications

References 67 publications

The performance comparison of the decision tree models on the prediction of seismic gravelly soil liquefaction potential based on dynamic penetration test

The performance comparison of the decision tree models on the prediction of seismic gravelly soil liquefaction potential based on dynamic penetration test

Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at An Agricultural Watershed

Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling

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