The purpose of this study is to conduct drought vulnerability assessment and cluster analysis of Korean island areas at eup (town) myeon (subcounty) level. Drought vulnerability assessment was conducted using factor analysis and entropy method, and cluster analysis was analyzed using K-means, a nonhierarchical cluster analysis method. Vulnerability consisted of climate exposure, sensitivity, and adaptive capacity. Twenty-two indicators were used to evaluate and analyze vulnerability of drought in small island areas. The results of entropy method showed that winter rainfall, no rainfall days, agricultural population rate, cultivation area rate, water supply rate and groundwater capacity have a substantial impact on drought assessment. The overall assessment of vulnerability indicated that Seodo-myeon Ganghwa-gun, Seolcheon-myeon Namhae-gun, and Samsan-myeon Ganghwa-gun were most vulnerable to drought. The cluster analysis was evaluated by categorizing the regions into three clusters, and policy support and planning are needed to suit the characteristics of each cluster was observed.

In this study, we developed a pilot-scale membrane capacitive deionization (MCDI) system for treating mildly brackish water and examined various operational parameters, including module arrangements, adsorption/desorption times, and flow rates. As we aimed to optimize these parameters to increase total dissolved solids (TDS) removal efficiency, the results revealed that the dual-series mode module arrangement and an adsorption time of 120 s with a flow rate of 10 L/min achieved the highest TDS removal efficiency of 99%. Energy consumption analysis showed that lower flow rates were associated with higher TDS removal efficiencies, highlighting the balance between energy consumption and water quality. This study provides insights into optimizing a pilot-scale MCDI for efficient water supply solutions, offering promise for sustainable and eco-friendly water treatment.

Coagulation is a critical unit process in drinking water treatment plants (DWTPs), where accurate dosing of coagulants such as polyaluminum chloride (PAC) and polyaluminum hydroxide chloride silicate (PACS) directly determines turbidity removal and operational stability. However, nonlinear interactions among water-quality variables complicate dosage prediction, and jar tests or operator heuristics cannot support real-time control. This study presents a scientifically interpretable and operationally transferable framework based on polynomial multiple linear regression (PMLR) with Lasso regularization, which was specifically developed for full-scale DWTP environments. While conventional PMLR rapidly overfits beyond polynomial degrees of 4–5, the Lasso-regularized model maintained stable generalization even at a degree of 10 by automatically pruning redundant terms and suppressing multicollinearity, thereby minimizing the need for manual hyperparameter tuning. Using 8303 hourly operational records from a full-scale DWTP in Korea, the Lasso-PMLR achieved R² = 0.951, RMSE = 0.120, and MAPE = 7.02%, outperforming traditional linear regression (R² = 0.896; MAPE = 8.64%). This proportional stability across increasing polynomial degrees, demonstrated directly using long-term real-world data, is particularly valuable for practical deployment because it ensures robustness without complex model-selection procedures. The transparent coefficient structure enables operators—who typically rely on jar tests—to understand and adjust dosing behavior, offering a field-ready and interpretable alternative to black-box models and supporting more efficient coagulant use, reduced sludge production, and sustainable automation in DWTP operation.

This study compares the operational performance of membrane capacitive deionization (MCDI) and circulation-MCDI (C-MCDI) under symmetric (2/2, 3/3, 4/4 min) and asymmetric (5/2, 5/3, 5/4 min) adsorption/desorption cycles to identify efficient operating conditions at the pilot scale. A pilot system was tested with a NaCl solution of about 1000 mg/L, and 15 consecutive cycles were conducted to evaluate removal efficiency, specific energy consumption (SEC), and stability. MCDI consistently achieved over 90% removal efficiency with SEC below 0.6 kWh/m³ across all modes, maintaining stable performance over 15 cycles. The 2/2 condition provided the shortest cycle time and the highest treated water productivity, making it the most efficient condition for the pilot-scale MCDI tested. C-MCDI showed stronger dependence on operating conditions, with the number of stable cycles ranging from 3 to 7 depending on desorption duration. Nevertheless, the 5/2 condition achieved about 91% removal efficiency with 0.58 kWh/m³ SEC, and its extended adsorption period yielded about 2.5 times more treated water per cycle than the 2/2 case. Overall, this work provides a comparative pilot-scale evaluation of MCDI and C-MCDI, highlighting their advantages, limitations, and potential applications, and offering practical insights for energy-efficient and sustainable desalination strategies.