Md. Abdullah-Al Mamun

Riverine floodplain communities face escalating risks under climate change, environmental degradation, and socio-economic vulnerability. This study develops a rigorously validated framework for assessing community resilience by integrating multi-criteria decision analysis, principal axis factoring, Monte Carlo–based index construction, deep learning validation, and explainable artificial intelligence. Using 1000 georeferenced observations and 56 multidimensional indicators across socio-cultural, economic, infrastructural, institutional, hydraulic, and ecological domains for 49 unions in the Brahmaputra River Basin (Bangladesh), the study constructs a Combined Multidimensional Resilience Index (CMRI). Deep learning models demonstrate high predictive capability (AUC = 0.989), while SHAP analysis identifies hydraulic deficits, ecological degradation, and institutional weaknesses as primary drivers of vulnerability.

This study presents the first integrated explainable and physics-informed AI framework combining ML, deep learning, and Physics-Informed Neural Networks (PINNs) to predict and interpret seasonal water quality dynamics in the Padma River Basin. Using 44 monitoring sites sampled during winter and monsoon seasons, the framework integrates WQI assessment, SHAP and game-theory attribution, probabilistic uncertainty analysis, and spatial autocorrelation. Seasonal variability dominates over spatial variability, with winter low-flow intensifying solute concentrations, while monsoon discharge promotes basin-wide dilution. PINN-based data augmentation improves model generalization, and explainable modeling identifies nitrate, dissolved oxygen, pH, and suspended solids as key seasonal drivers.

Coastal aquifers in southern Bangladesh are increasingly threatened by groundwater overexploitation, saltwater intrusion, and diffuse contamination. This study proposes a hybrid, data-driven, and interpretable modeling framework integrating machine learning, statistical analysis, and spatial assessment to predict nitrate (NO₃⁻) and sulfate (SO₄²⁻) risks in coastal aquifers. The framework identifies dominant hydrogeochemical drivers, evaluates spatial contamination hotspots, and improves interpretability of prediction outcomes to support groundwater risk management and evidence-based decision-making in vulnerable coastal environments.

This study used traditional statistical techniques (PCA, PCoA) together with machine-learning models including Self-Organizing Maps (SOM), Conditional Inference Trees (CIT), Ridge Regression (RR), and SHAP to detect and trace heavy metal sources along the northeast coast of Bangladesh. Concentrations of Pb, Cd, Mn, and As exceeded average shale values. SOM indicated natural sources for As, Ni, Fe, and Mn, and shipbreaking industry sources for Cu, Zn, and Pb. SHAP highlighted soil pH, silt, and organic matter as key contributors to HM accumulation.

This work investigates Gaussian Process Regression (GPR), Bayesian Ridge Regression (BRR), and Artificial Neural Network (ANN) for predicting groundwater quality in coastal Bangladesh. Optuna-based optimized hyperparameters improve model accuracy. Combined cross-validation and bootstrapping methods were used to build six predictive models. The entropy-based ECWQI was converted into five quality classes. SOM and spatial autocorrelation identified four distinct spatial patterns. ANN (CV) and ANN (B) models outperformed all others (R² = 0.971 and 0.969 respectively). SO₄²⁻, Cl⁻ and F⁻ played an important role in prediction accuracy.

This work combines geostatistical modeling, self-organizing maps (SOM), and data-driven algorithms to determine the driving factors and predict groundwater phosphate (PO₄³⁻) content in coastal multi-aquifers in southern Bangladesh. Four algorithms — CatBoost, GBM, LSTM, and SVR — were evaluated using 380 samples and 15 prediction parameters. The CatBoost model showed exceptional performance in both training (R² = 0.999) and testing (R² = 0.989). Phosphate dissolution and saltwater intrusion, along with phosphorus fertilizers, increase PO₄³⁻ content in groundwater. Na⁺, K⁺, and Mg²⁺ significantly influenced prediction accuracy.