Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 2111-2116· 0 citations· 19 references
Abstract
Significant economic and ecological harm can result from harvesting operations that are not timed appropriately, especially when the number of vehicles involved exceeds the soil's holding capacity. This causes changes in nutritional and water conditions, compaction of the soil, and damage to tree roots and stems. The need for improved data on soil properties, particularly bearing capacity, is underscored by the fact that deep ruts created by vehicle movement further impede forest activities. First, the data was normalised for preprocessing in this study. Then, features were extracted using skewness, kurtosis, standard deviation, RMS, and crest factor. To forecast soil type and bearing capacity, the AdaBoost-SVM-KNN model was employed. This model optimises the parameters of SVM and adaptively modifies the parameters of KNN kernels in order to formulate component classifiers that are efficient. A weighted forecast was produced by averaging the predictions of the two models after the SVM updated the original KNN weights. An astounding 96.36% accuracy rate was shown by the results, proving that the AdaBoost-SVM-KNN model is capable of accurately soil classification and bearing capacity prediction. Better decision-making and the promotion of more sustainable forest management techniques could help reduce the negative effects of unsuitable harvesting activities.
Abstract. Rainfall-induced landslides (RIL) are a major hazard in the Southern Andes, threatening lives, infrastructure, and ecosystems. Early warning systems require accurate predictive models; however, their effectiveness is constrained by heterogeneous data availability and the lack of universal design standards. This study develops a systematic framework to identify the most influential features controlling landslide generation by integrating local soil, climatic, and topographic datasets. A national landslide inventory was expanded using Buffer Control Sampling and PUBagging to improve the representation of non-landslide cases, yielding a robust database of 3148 instances with 136 variables. Feature selection was performed using Classification and Regression Trees (CART) and, in parallel, Genetic Algorithms (GA), with both approaches evaluated using Support Vector Machines, Random Forest, and XGBoost classifiers. Results highlight precipitation, slope, and soil hydraulic properties – particularly bulk density and saturated water content – as recurrent critical predictors. GA-based models significantly outperformed CART, with GA-RF and GA-XGB achieving the lowest error rates (10.95 %) while using compact feature sets. These findings underscore the potential of evolutionary feature selection to enhance predictive accuracy while reducing data complexity, and they provide actionable insights into which variables should be prioritised in monitoring networks. This work contributes to the design of more reliable and region-specific early warning systems for rainfall-induced landslides, emphasising the role of shallow and deep water storage features in mountainous environments.
Manuel Labbé, Millaray Curilem, Ivo Fustos-Toribio et al.· Natural Hazards and Earth Sy...· 0 citations
The soil‐water characteristic curve (SWCC) is a fundamental parameter that governs the hydro‐mechanical behavior of unsaturated soils. Conventional laboratory measurement of SWCC is time‐consuming and labor‐intensive, while traditional lateral earth pressure design for retaining walls frequently relies on the saturated soil assumption, neglecting the effects of SWCC and resulting in significant systematic deviations in calculations. This study develops a statistically rigorous machine learning (ML) framework for efficient SWCC prediction and its application to lateral earth pressure calculations for pile‐supported box counterfort retaining walls. Four ML algorithms with distinct methodological frameworks were employed: extreme learning machine (ELM), least squares support vector machine (LSSVM), projection pursuit regression (PPR), and Bayesian ridge regression (BRR). These algorithms were utilized to construct SWCC prediction models using the cleaned UNSODA database. Model performance was assessed through multi‐metric evaluation, paired
t
‐tests for statistical significance, and robustness analysis involving 30 independent runs, with validation conducted on measured silty clay data across 12 suction levels. Results indicate that the ELM model achieves the highest prediction accuracy, demonstrating statistically significant superiority over LSSVM, PPR, and BRR and excellent robustness. Independent validation reveals an average relative error of only 2.25% for ELM‐predicted SWCC. The SWCC‐based earth pressure calculation rectifies the bidirectional deviations of the traditional saturated method and identifies a neutral point at a depth of 17.5 m for a 25 m‐high retaining wall. This study offers a reliable technical approach for rapid SWCC acquisition and refined lateral earth pressure design for retaining structures.
Cheng Chang, Xiaobin Mu, Libin Han et al.· Applied Research· 0 citations
Biochar is a porous, carbon-rich soil amendment that can enhance soil water retention capacity by modifying pore structure and physicochemical properties. Understanding the soil−water characteristic curve (SWCC) of biochar-amended soils is essential for evaluating their hydrological behavior and promoting the application of biochar in engineering practice. Given the demonstrated feasibility and accuracy of machine learning methods for predicting soil parameters, this study employed six machine learning models, namely, decision tree, random forest, XGBoost, LightGBM, CatBoost, and artificial neural network, to predict the SWCC of biochar-amended soils based on a constructed dataset. Feature importance analysis and partial dependence analysis were further conducted to reveal the influence patterns of key variables. The results indicate that all six models exhibit good predictive capability, with gradient boosting models (XGBoost, CatBoost, and LightGBM) performing best. Suction is the dominant factor controlling the volumetric water content variation, while soil particle-size distribution and dry density provide the physical basis for water retention. Biochar content, pyrolysis temperature, and feedstock type further modulate the water retention capacity of amended soils. Overall, the findings demonstrate that machine learning approaches can effectively predict the SWCC of biochar-amended soils and provide insights into the controlling mechanisms of soil water retention.
Soil water availability is one of the major challenges in many rainfed crop production systems of the Global South. Soil water conservation practices are being promoted to enhance climate change adaptation for rainfed cropping systems of southern Africa. However, the cost and time required to develop and test appropriate modelling and simulation tools can be enormous. The objectives of this study were to: (i) test the performance of the decision tree, adaptive boosting (AdaBoost), support vector machine, neural network, stochastic gradient descent, k-nearest neighbours, random forest and linear regression machine learning models in predicting soil water under different tillage practices, soil types and depths, and (ii) assess the soil water classification and prediction capabilities of 8 models under different tillage practices, soil types and depths. The neural network, random forest and decision tree models had the best soil water prediction capabilities. The neural network, random forest and decision tree models were the best algorithms (RMSE = 15.801–16.369; MAE = 11.997–12.315; R2 = 0.822–0.835) for predicting and classifying soil water from different soil types and depth intervals. The support vector machine learning model was the weakest algorithm (RMSE = 36.177; MAE = 30.84; R2 = 0.133) for predicting and classifying soil water. All the algorithms poorly predicted and classified soil water based on tillage practices. All the models closely predicted soil water at 300 and 900 mm depths but poorly predicted soil water at 600 mm depth intervals. Based on this study, the neural network model is the best machine learning tool for predicting soil water in clay and sandy soils under semi-arid agroecological conditions.
W Mupangwa, L Chipindu, B Ncube et al.· Water S.A· 0 citations
Accurate prediction of wetted width and wetted depth is essential for optimizing water use efficiency in drip irrigation systems. Existing empirical models are often restricted to specific soil textures and cannot adequately capture the complex nonlinear interactions among soil hydro-physical and chemical properties, irrigation variables, and different soil textures. This study evaluated four machine learning algorithms—Linear Support Vector Machine (Linear SVM), Medium Gaussian Support Vector Machine (Medium Gaussian SVM), Matern 5/2 Gaussian Process Regression (GPR), and Boosted Tree Regression—for predicting wetted width and wetted depth in sand and sandy loam soils. Model inputs included emitter discharge, irrigation duration, and selected soil hydro-physical and chemical properties. Models were developed using a 70% training dataset and validated with the remaining 30%. The Matern 5/2 GPR achieved the highest training accuracy for wetted width (R2 = 0.99; RMSE = 0.74) and wetted depth (R2 = 0.98; RMSE = 0.90), but validation errors increased to RMSE values of 2.27 and 3.84, respectively. Medium Gaussian SVM yielded the lowest validation RMSE (2.11) for wetted width, whereas Boosted Tree Regression achieved the best wetted depth prediction (RMSE = 2.11; MAE = 1.69). These findings demonstrate the importance of model-specific selection for reliable irrigation management.
O. Faloye, O. M. Abioye, A. Okunola et al.· Hydrology· 0 citations
Accurate prediction of maximum dry density (MDD) and optimum moisture content (OMC) is critical for effective compaction control and earthwork design in geotechnical engineering. Conventional laboratory compaction tests are time-consuming and resource-intensive, motivating the adoption of reliable data-driven prediction models. In this study, a hybrid modeling framework integrating the Rao-1 metaheuristic optimization algorithm with Artificial Neural Network (ANN), Random Forest (RF), and Gradient Boosting (GB) models is proposed for predicting MDD and OMC. A dataset comprising 397 soil samples, characterized by gradation properties and Atterberg limits, is utilized for model development and validation. The Rao-1 algorithm is employed to optimize network weights and model hyperparameters, aiming to enhance convergence behavior and predictive accuracy. Comparative results reveal that Rao-1 optimization consistently enhances model performance across all algorithms and target variables. For MDD prediction, the ANN model achieves an increase in R2 from 0.8512 to 0.9277, accompanied by a reduction in RMSE from 0.4327 to 0.2865. Similarly, the RF and GB models show notable improvements, with optimized R2 values reaching 0.9176 and 0.9213, respectively. For OMC prediction, the Rao-1 optimized ANN exhibits the highest accuracy, improving R2 from 0.8234 to 0.9245 and reducing RMSE from 0.4677 to 0.3071, while optimized RF and GB models also demonstrate substantial error reductions. Furthermore, SHapley Additive exPlanations (SHAP) and the Cosine Amplitude Method (CAM) were integrated to enhance model interpretability, enabling transparent evaluation of feature contributions and providing deeper insight into the influence of geotechnical parameters. Overall, the proposed Rao-1-based hybrid framework significantly enhances predictive accuracy and error minimization compared to conventional models. The results confirm the robustness and effectiveness of Rao-1 optimization in data-driven soil compaction modeling, offering a practical decision-support tool for process innovation and the preliminary estimation of compaction parameters, rather than replacing standardized laboratory testing.
Bayram Ateş, J. Tiang, M. A. Eirgash et al.· Scientific Reports· 0 citations