Soil erodibility (K) is an essential component in estimating soil loss indicating the soil's susceptibility to detach and transport. Data Computing and processing methods, such as artificial neural networks (ANNs) and multiple linear regression (MLR), have proven to be helpful in the development of predictive models for natural hazards. The present case study aims to assess the efficiency of MLR and ANN models to forecast soil erodibility in Peninsular Malaysia. A total of 103 samples were collected from various sites and K values were calculated using the Tew equation developed for Malaysian soil. From several extracted parameters, the outcomes of correlation and principal component analysis (PCA) revealed the influencing factors to be used in the development of ANN and MLR models. Based on the correlation and PCA results, two sets of influencing factors were employed to develop predictive models. Two MLR (MLR-1 and MLR-2) models and four neural networks (NN-1, NN-2, NN-3, and NN-4) optimized using Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) were developed and evaluated. The model performance validation was conducted using the coefficient of determination (R²), mean squared error (MSE), root mean squared error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE). The analysis showed that ANN models outperformed MLR models. The R² values of 0.446 (MLR-1), 0.430 (MLR-2), 0.894 (NN-1), 0.855 (NN-2), 0.940 (NN-3), and 0.826 (NN-4); MSE values of 0.0000306 (MLR-1), 0.0000315 (MLR-2), 0.0000158 (NN-1), 0.0000261 (NN-2), 0.0000318 (NN-3), and 0.0000216 (NN-4) suggested the higher accuracy and lower modelling error of ANN models as compared with MLR. This study could provide an empirical basis and methodological support for K factor estimation in the region.

One of the complex processes in spectral analysis of surface waves (SASW) data analysis is the inversion procedure. An initial soil profile needs to be assumed at the beginning of the inversion analysis, which involves calculating the theoretical dispersion curve. If the assumption of the starting soil profile model is not reasonably close, the iteration process might lead to nonconvergence or take too long to be converged. Automating the inversion procedure will allow us to evaluate the soil stiffness properties conveniently and rapidly by means of the SASW method. Multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and linear regression (LR) algorithms were implemented in order to automate the inversion. For this purpose, the dispersion curves obtained from 50 field tests were used as input data for all of the algorithms. The results illustrated that SVR algorithms could potentially be used to estimate the shear wave velocity of soil.

Geotechnical engineering design necessitates a study of soil parameters for calculation purposes. As a complement to the conventional method, a resistivity survey can give preliminary results in a short amount of time. This study uses a statistical analysis program to show a correlation and regression for predicting soil parameters based on the resistivity value. Sixty-six data points for each soil parameter and resistivity were used for statistical analysis from six profiles of the study area as a part of the investigation. The soils are sampled by drilling the borehole up to a depth of 3 meters. The Wenner array was applied to conduct a resistivity survey. The collected data were utilized to establish a correlation between resistivity (ρ) and soil parameters such as moisture content (), clay content (clay), plasticity index (), friction angle (ϕ), bulk density (γ), and porosity (n). The generated statistic model was improved through several iterations for each correlation. For the ρ-, ρ-clay, ρ-PI, ρ-ϕ, ρ-γ, and ρ-n relationship, the total iterations are 5, 4, 7, 6, 5, and 4 for each correlation, respectively. Resistivity and moisture content showed a significant power correlation, followed by a modest relationship between resistivity and porosity. Between resistivity and other parameters, relationships such as clay content, bulk density, friction angle, and plasticity index were found to have very weak or no association. However, some trends can be seen clearly. This study aims to offer the geotechnical engineer a quick preliminary way of obtaining the associated soil parameters for various geotechnical calculations employing the resistivity approach based on the developed numerical equation.