Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.
In highway slopes (HWS) constructed on high expansive clay soil (HECS), in situ moisture variation is an environmentally driven variable that can significantly impact the safety of the constructed soil. Electrical resistivity imaging (ERI) is a non-destructive method with a considerable potential for subsurface soil moisture mapping, which can be correlated with volumetric soil moisture content (VSMC) and soil matric suction (SMS) of HECS to remarkably enhance the evaluation of the performance of the HWS. However, limited datasets are available to evaluate the accuracy and feasibility of the available correlative field-based models for the HECS under various field conditions. The objective of the current study is to develop a field-based model of VSMC and SMS using real-time field monitoring and ERI data. Six HWS located in the Jackson metro area in Mississippi (MS), USA were considered as reference slopes in this study. Comprehensive field instrumentation was executed at the six HWS to monitor the VSMC, SMS and rainfall intensity. The sensors were installed at the crest, middle and toe of the slope. The 2D ERI test was conducted using a dipole–dipole array with multiple electrodes at 5 ft (1.5 m) spacing. The ERI survey was conducted at the crest and middle of the six HWS to image the continuous soil subsurface profile in terms of moisture variation. The developed models indicated a good agreement between instrumented and ERI data. The developed models will facilitate the estimation of VSMC and SMS variations and aid in performance monitoring of the HWS built on HECS such as Yazoo clay.
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