Rapid urbanisation, agricultural intensification, and climate variability have accelerated the deterioration of groundwater and surface-water quality worldwide. Traditional assessment methods—largely dependent on laboratory analysis and deterministic modelling—are often time-consuming, data-limited, and incapable of capturing the nonlinear interactions governing hydro-chemical processes. In response, Artificial Intelligence (AI) and Machine Learning (ML) techniques have emerged as transformative tools for water-quality prediction, classification, and decision support.This survey synthesises findings from over forty peer-reviewed studies (2019–2025) covering statistical, geostatistical, supervised, unsupervised, and deep-learning approaches applied to groundwater and surface-water datasets. We systematically compare our scope and contributions with four major existing surveys, demonstrating that this review uniquely integrates (i) a five-family taxonomy spanning statistical methods through hybrid-optimisation frameworks, (ii) an explicit communication-layer perspective linking IoT sensor networks, edge computing, and green ICT to water-quality intelligence, and (iii) applied explainable-AI (XAI) and physics-informed learning analysis absent from prior work.The review evaluates model architectures including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and hybrid neuro-fuzzy and genetic-optimisation frameworks. Reported accuracies frequently exceed 95%, with ensemble and deep models outperforming classical statistical regressions in predicting the Water Quality Index (WQI) and contamination risk. A concrete prediction-task formulation distinguishes regression from classification and anomaly detection, specifying typical input features, temporal horizons, and evaluation protocols. Beyond accuracy, emerging trends emphasise explainability (SHAP, LIME), feature selection (entropy-correlation, PCA), and spatial integration with GIS and remote sensing. Despite remarkable advances, persistent challenges include data imbalance, regional transferability, and limited interpretability of complex models. The review concludes by outlining future research needs in interpretable hybrid frameworks, physics-informed learning, and real-time AI–IoT water-quality management.
Unmanned aerial vehicles (UAVs) have emerged as an effective platform for disaster monitoring due to their ability to capture high-resolution imagery in complex and hard-to-reach environments. However, accurate disaster recognition remains challenging due to distributed data collection and heterogeneous data distributions across UAVs. In this work, we present a Federated Vision-Language Prompting (FedVLP) for UAV-based disaster recognition that enables multiple UAV clients to collaboratively train a shared model without centralized data aggregation. To improve robustness under imbalanced and non-IID data conditions, the proposed approach incorporates class-imbalance aware training and a Dirichlet-based data partitioning strategy. Furthermore, a vision-language prompting mechanism is introduced to enhance feature representations by aligning visual features with class-level semantic descriptions, improving discrimination between visually similar disaster categories. A lightweight backbone network is employed to ensure computational efficiency for resource-constrained UAV platforms. Experimental results on the AIDERv2 and DisasterEye datasets demonstrate that the proposed framework achieves superior performance compared with strong baselines while maintaining a favorable efficiency–accuracy trade-off. These findings highlight the effectiveness of integrating federated optimization with semantic guidance for scalable UAV-based disaster recognition.
