Machine Learning Algorithm-Based Prediction of Groundwater Table in Bogura District, Bangladesh

Refined simulation of groundwater level dynamics based on a machine learning stacking framework

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.

Research on water level prediction considering uncertainty quantification based on EMD-PSO-RBFNN hybrid model

With the rapid development of inland waterway transportation, the demand for reliability and accuracy in hydrological forecasting has been increasing. Inland water levels are affected by rainfall, upstream flood discharge, and seasonal factors, exhibiting highly nonlinear and non-stationary characteristics. Traditional deterministic prediction models can hardly meet the requirements of refined scheduling. This paper proposes a hybrid water level prediction model that integrates Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Radial Basis Function Neural Network (RBFNN), and innovatively introduces an uncertainty quantification mechanism based on the statistic. First, EMD is used to decompose the original complex water level signal into multiple Intrinsic Mode Functions (IMFs) to reduce data nonstationarity. Second, for each IMF component, the PSO algorithm is adopted to globally optimize the centers and spread constants of the RBF neural network, constructing high-precision base prediction models. Finally, the uncertainty coefficient is calculated. Using actual water level data from the Port of Guigang as the experimental object, the results show that the prediction accuracy of the hybrid model reaches R2=0.9415, and the α coefficient can effectively quantify the dynamic risk of prediction results. This study not only provides a high-precision technical approach for inland water level prediction, but its uncertainty quantification results also offer a scientific basis for the reliability evaluation and risk early warning of hydrological forecasting.

Comparing the Performance of Single and Hybrid Machine Learning Models for Estimating Pan Evaporation Across Climatically and Structurally Diverse Reservoirs

Hybrid Particle Swarm Optimization and Machine Learning Framework for Enhanced Prediction of Ground Settlement during Rectangular Pipe Jacking

Accurate prediction of ground settlement induced by rectangular pipe jacking, a prevalent trenchless technology in urban infrastructure development, remains a significant challenge. This study addresses this by developing and evaluating a robust machine learning (ML) framework. Leveraging 104 sets of field monitoring data from the Liuye Avenue West Extension rectangular pipe jacking project in Hunan, China, key construction parameters including jacking force, advance rate, and grouting pressure were utilized as inputs to predict ground settlement. A Particle Swarm Optimization (PSO) algorithm was integrated for automated hyperparameter tuning of six distinct ML models: standalone Least Squares Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), Random Forest (RF), and their respective PSO-optimized counterparts. Comprehensive performance evaluation using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R^2) revealed that the PSO-LSSVM hybrid model achieved superior predictive accuracy and generalization capability. Specifically, on the test dataset, the PSO-LSSVM model yielded an MSE of 0.367, MAE of 0.424, and an R^2 of 0.941. These findings demonstrate that the proposed PSO-enhanced LSSVM model significantly outperforms baseline models, offering a highly effective and reliable tool for predicting ground deformation in similar complex pipe jacking projects.

Machine Learning-Based Groundwater Level Forecasting in a Semi-Arid Agricultural Area: Insights from SHAP, PELT, and Mann–Kendall Analyses in the Saïss Basin, Morocco

This study proposes an innovative framework that combines hydroclimatic and agro-environmental predictors, including nitrate concentration and NDVI, with climatic factors such as Rainfall, temperature, and evapotranspiration to forecast piezometric level variations in the Saïss Basin, Morocco. Eight Machine Learning (ML) models were benchmarked, and feature importance was assessed using Shapley Additive exPlanations (SHAP) to ensure model transparency and interpretability. In parallel, the PELT algorithm was applied to detect structural change points, while Sen’s slope estimator and the Mann–Kendall test were used to quantify long-term trends. The Extra Trees (ET) model achieved the best performance (R2 = 0.92), with Rainfall emerging as the most influential predictor, followed by nitrate concentration, confirming the added value of hydrochemical indicators for groundwater forecasting. Change-point analysis revealed significant declines during the 1980s and 1990s, followed by lower-amplitude fluctuations since the late 2000s. Projections toward 2050 suggest partial stabilization in the central part of the basin under favorable recharge conditions, whereas persistent declines are expected to continue in peripheral areas subjected to sustained groundwater abstraction pressure. These findings provide a robust and transferable decision-support tool for the sustainable management of groundwater resources in semi-arid agricultural area.

Explainable artificial intelligence for groundwater quality prediction and hydrochemical interpretation.

Over the past decades, groundwater quality has declined significantly due to rapid urbanization, excessive fertilizer usage, and climate-driven hydrogeochemical changes. Accurate prediction and interpretation of groundwater quality therefore require advanced data-driven approaches integrated with domain knowledge. This study proposes an interpretable hybrid artificial intelligence framework for groundwater quality prediction and analysis using explainable artificial intelligence (XAI) and advanced machine learning algorithms. A total of 135 groundwater samples were collected from Tamil Nadu, India and analysed for 16 physicochemical parameters. The dataset was pre-processed through data cleaning, feature scaling, and outlier detection. Several machine learning models, including XGBoost, Random Forest, LightGBM, and CatBoost, were optimized using Optuna-based hyperparameter tuning and integrated through a stacked ensemble framework. The meta-learner achieved the best predictive performance (R2 = 0.938, RMSE = 0.237, MAE = 0.184). Water Quality Index analysis identified ammonia (NH₃), iron (Fe), and chromium (Cr) as dominant contaminants. Model interpretability using SHAP, LIME, and permutation importance revealed the buffering role of hydrochemical ions such as HCO₃⁻ and Ca2⁺, while spatial AI mapping indicated localized industrial contamination. The proposed framework improves predictive reliability while providing interpretable insights to support sustainable groundwater management.