Flooding remains one of the most damaging climate‐related hazards globally, yet flood remote sensing has historically developed largely along sensor‐specific pathways. This review addresses the need for a systematic and decision‐oriented synthesis of how space‐borne remote sensing has evolved toward integrated flood monitoring and disaster risk reduction frameworks. Moving beyond conventional sensor inventories, the study examines sensor integration pathways, temporal evolution, and decision relevance. Four research questions guide the analysis: (RQ1) how space‐borne sensors have been applied and advanced in flood studies, (RQ2) how multi‐sensor fusion architectures have evolved, (RQ3) what limitations persist, and (RQ4) what future directions are emerging. Following PRISMA guidelines, 176 peer‐reviewed studies published between 2001 and 2024 were systematically analysed. Quantitative synthesis indicates a marked post‐2018 increase in multi‐sensor approaches, alongside a growing share of studies adopting fusion frameworks. Synthetic Aperture Radar (SAR)‐centred integration systems represent a dominant share of recent applications; most commonly combined with precipitation products and digital elevation models. Analytical synthesis further indicates that SAR plays a central role in flood detection, while precipitation and topographic data provide key hydrological drivers and terrain constraints. These findings indicate a transition from static flood mapping toward process‐aware, multi‐sensor, and algorithm‐driven flood intelligence systems. The review also highlights the growing role of artificial intelligence and cloud‐based platforms in enabling scalable, near‐real‐time flood analysis. It concludes that future flood resilience increasingly depends on the coordinated integration of space‐borne observations, advanced analytics, and operational decision‐support architectures. This synthesis provides a unifying framework to guide next‐generation flood monitoring and disaster risk reduction under accelerating climate change.
R. Kemarau, Nurfashareena Muhamad, Aida Soraya Shamsuddin et al.· Journal of Flood Risk Manage...· 0 citations
The primary contamination concern of mineral processing tailings (MPT) is the leaching of hazardous substances into the environment. The literature indicates that MPT requires effective flocculation and polymer-assisted dewatering to ensure its disposal does not cause environmental damage. In this research, a theoretical modeling framework was adopted, based on the development of a hybrid machine learning (ML) model for predicting flocculation-dewatering efficiency, aiming to reduce the cost of laboratory tests. The proposed ML model is based on an efficient Gaussian process regression (GPR) model that uses a feature impact strategy (FIS) and a kernel matrix dynamically updated using error noise, named enhanced GPR (EGPR). Additionally, the kernel ridge model and SHAP (SHapley Additive exPlanations) method are coupled for feature selection (FS), identifying the most important features among 17 input variables (features). The target variable in this research is the initial settling rate (ISR), which is predicted to utilize the EGPR model. The statistical analysis revealed that the EGPR model outperforms the Deep random vector functional link (DRVFL), least square support vector machine (LSSVM), cascade feedforward neural network (CFNN), and ridge regression with superior error metrics (R = 0.951, RMSE = 0.196, MAPE = 62.22). It also demonstrated the least uncertainty (UI = 17.65), which indicates its reliability and accuracy. Risk analysis (RA) indicates that the EGPR model yields the lowest total risk score (TRS) (5.6) and is classified as a “Very Low” risk method. Furthermore, SHAP analysis exhibits that the solids content (SC) and flocculant dose (FD) positively influenced the prediction of ISR. Consequently, this study presents a foundational methodology for predicting ISR, which could be introduced as an essential tool for flocculate-settling studies that contribute to optimal chemical dosage, real-time contamination monitoring, and the operation of water recovery storage capacity.
Z. Yaseen, Najeebullah Khan, S. Shahid et al.· Scientific Reports· 0 citations
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