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Multi-purpose dams in a river basin frequently result in variations in downstream flow. Precisely assessing the reservoir operation effects can improve management strategies and alleviate extreme hydrological events. This study assesses the impact of reservoir operation scenarios on the downstream flow in the Seomjin River basin in South Korea. Four reservoir scenarios were developed utilizing observed daily inflow and outflow data from the reservoirs. A semi-disturbed hydrological model, SWAT (Soil and Water Assessment Tool), was employed to simulate the flow for each reservoir operation scenario in the downstream section of the study basin. Model execution was evaluated by comparing the simulated and measured streamflows using performance metrics, including R2, NSE, and PIBAS, which displayed very good compatibility. The sensitivity of calibration parameters varied across different reservoir operation scenarios. The results of this study indicate that the operation scenarios for the Seomjin and Juam reservoirs led to a maximum downstream flow reduction of 32%. Additionally, the monsoon season exhibited a lower percentage reduction in flow compared to the dry season, which was influenced by the frequency of rainfall in the region. Annual assessment indicated that streamflow reduction varies between 1.35% and 32.9% across all reservoir operation scenarios. Reservoir operations have demonstrated their effect on the alteration of downstream flow in the Seomjin River basin. This study demonstrates that the operation of the Seomjin reservoir has a more significant impact on downstream flow than that of the Juam reservoir in the study region. This study analyzed a substantial basin with various reservoir operation scenarios to assess the influence of flow on the downstream section, yielding important insights for efficient water resource management. [ABSTRACT FROM AUTHOR]

The excessive extraction of groundwater is a globally significant issue, as it can lead to the permanent loss of groundwater system sustainability. Sustainable groundwater requires development that appropriately balances the needs of both humans and the environment. In this study, the exploitable groundwater (EGW) of the So-Yang-gang Dam (SYD) Basin was estimated based on simulated groundwater recharge rates using SWAT, and the sustainability of future groundwater development was evaluated under different climate change scenarios. The EGW in each sub-watershed of the SYD was estimated to range from 60 to 240 mm/year, with higher values in the upstream watersheds. A sustainability index (SI) was evaluated, ranging from 0.56 to 1.0 across various GCMs. The analysis revealed that uniform EGW across a watershed is overestimated value in sub-watersheds with low recharge rates, potentially accelerating groundwater depletion in those areas. Thus, a flexible EGW estimation approach is essential to balance groundwater conservation with human water demands. [ABSTRACT FROM AUTHOR]

Recent developments in real estate brokerage platforms have enabled residents to provide subjective reviews, which have immense value as subjective assessments and suggestions for architects. This study suggests a deep-learning-based natural language sentiment classification model to analyse reviews. Morpheme analysis and word embedding for 'KoNLPy' and 'Word2vec' were structured for pre-processing, and a long short-term memory network was used to process review data. Total 5974 review data were used in this study. Among the various active online platforms for real estate brokerage, platforms that provide online users with the ability to write reviews of their living spaces were crawled. The review data were classified as 'positive' or 'negative' by label and as 'Apartment' or 'Non-Apartment' by housing type. The model developed in this study is expected to increase in value as more online platforms appear in the future and the volume of natural language data generated by those platforms increases. [ABSTRACT FROM AUTHOR]