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.
The precise estimation of spun pile efficiency parameters through High Strain Dynamic Testing (HSDT) is critical for structural safety and foundational integrity; however, it is frequently constrained by significant economic, temporal, and logistical limitations that restrict physical testing to a minor fraction of installed piles. To overcome these prohibitive barriers, this study proposes a high-fidelity hybrid predictive framework that synergizes advanced machine learning architectures with nature-inspired metaheuristic optimizers. Three distinct predictive models: Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), and Regression Tree (RT) were formulated. To resolve the inherent limitations of suboptimal convergence and hyperparameter trapping in standard configurations, the structural parameters of these models were optimized using the Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). Utilizing a high-quality, rigorously validated dataset of 150 Pile Driving Analyzer (PDA) records, the models were trained to forecast the maximum case method capacity (RMX) and maximum compressive force (FMX). To ensure model robustness and completely eliminate small-sample bias, a rigorous 10-fold bootstrapping cross-validation protocol was implemented, alongside a SHapley Additive exPlanations (SHAP) sensitivity analysis and Wilcoxon signed-rank testing for non-parametric statistical validation. The comparative benchmarking confirms the absolute superiority of the SSA-ANFIS framework, which achieved unprecedented predictive precision with a perfect correlation (R2=1.000) and minimal error profiles for both RMX (RMSE=0.123) and FMX (RMSE=0.506), statistically outperforming baseline models such as eXtreme Gradient Boosting (XGBoost) and the empirical Danish Driving Formula. For practical field deployment, the optimized architecture was embedded into a MATLAB-based Intelligent Geotechnical Decision Support System (IGDSS) and subjected to independent validation on a geologically distinct site, verifying its exceptional generalization capabilities. The integration of swarm intelligence with neuro-fuzzy logic presents a highly reliable, physically explainable, and cost-effective alternative to ubiquitous physical testing, advancing the paradigm of digital geotechnical engineering.
Rufaizal Che Mamat, A. Ramli, Muhammad Nasim Abdul Ghani· Current Problems in Research· 0 citations
This study proposes advanced stacking ensemble machine learning approaches to predict soil Liquidity Index and Undrained Shear Strength and highlights the robust potential of ensemble modeling and tailored optimization in the field of geotechnical engineering.
Giovanni Spagnoli, Mohammadreza Mahmoudi, S. Shimobe et al.· E3S Web of Conferences· 0 citations
Soil health management plays a central role in achieving sustainable agricultural productivity; however, conventional soil testing and fertilizer advisory practices are often labor-intensive, costly, and limited in precision. To overcome these limitations, this work presents a Hybrid Machine Learning and Optimization Framework for predictive soil health maintenance. The proposed framework integrates multiple predictive tasks namely soil nutrient estimation using NPK regression, fertilizer recommendation through classification, Soil Health Index (SHI) computation, and crop suitability prediction within a unified and data-driven decision-support architecture. The predictive modeling layer employs an ensemble of Random Forest, XGBoost, and Deep Neural Network (DNN) models to capture complex nonlinear relationships between soil and environmental parameters. To enhance predictive performance and generalization, model hyperparameters are automatically tuned using a Genetic Algorithm (GA), enabling efficient exploration of the hyperparameter search space. Comprehensive experiments conducted on a real-world soil dataset demonstrate that the proposed optimization-enhanced models consistently outperform baseline approaches.
For soil nutrient prediction, the Hybrid DNN + GA model achieved an approximate 20% reduction in RMSE compared to the baseline DNN, attaining a coefficient of determination R2 of 0.94. In the fertilizer recommendation task, the proposed approach achieved 94.2% classification accuracy with an ROC–AUC of 0.96, while crop suitability prediction reached an accuracy of 92.8%, representing a 4.6% improvement over conventional DNN-based models. The derived Soil Health Index exhibited a mean value of 0.78, with 58% of soil samples classified as Healthy, demonstrating the framework’s ability to translate complex predictions into an interpretable soil vitality indicator. The integration of predictive modeling, metaheuristic optimization, and interpretable soil health assessment enables reliable and actionable decision support for precision agriculture. The proposed framework offers a robust, scalable, and interpretable solution for proactive soil health management and efficient fertilizer utilization, contributing toward sustainable agricultural practices.
Ganeshwari Patil, D. G. Bhalke, Nilakshee Rajule et al.· International journal of com...· 0 citations
Accurate and rapid prediction of groundwater levels (GWL) is essential for effective groundwater management. Machine learning models are efficient tools for GWL prediction, but individual models often suffer from limited generalization due to inherent randomness. This study proposed a stacking-based GWL prediction framework suitable for arid regions in Northwest China. Feature variables affecting GWL were selected using variable importance in projection (VIP). Then, three machine learning models—artificial neural networks (ANN), random forests (RF), and Light Gradient Boosting Machine (LightGBM)—were developed, and their outputs were integrated using a support vector regression (SVR)-based stacking method to enhance the accuracy of GWL prediction. The results show that the factors of influencing GWL changes vary significantly across different regions, and selecting the most contributive feature variables is beneficial for model construction. Among the individual models, the RF model demonstrated higher accuracy and more stable performance, outperforming the ANN and LightGBM models. However, individual models exhibited poor generalization during validation. In contrast, the stacking model maintained high performance, demonstrating superior generalization. Compared to the best-performing individual model (RF) in validation period, the Nash–Sutcliffe efficiency (
NSE
) and Kling–Gupta efficiency (
KGE
) of stacking model improved by 0.11–0.66 and 0.05–0.41, the correlation coefficient (
R
2
) increased by 0.05–0.3, and root mean square error (
RMSE
) reduced by 0.01–0.1 m. In the stacking simulation, RF had the highest average contribution (80.2%), followed by ANN (13.9%) and LightGBM (5.9%). This study provides a stacking simulation framework based on machine learning methods for precise groundwater level simulation, which can serve as a reference for groundwater level simulation in other regions.
Xunzhen Cui, Xiaoxia Du, Haixia Dong et al.· Frontiers in Water· 0 citations
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.
K. Vijai, Mohanraj R, Nicson Lijo J et al.· 2026 7th International Confe...· 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