The river environment where people, animals, and plants exist together is a significant place to continue their own lives. Especially, since the river water quality directly impacts the survival of living things, it is crucial to effectively manage the quality of river water. To manage the river water quality effectively, it is important to make appropriate water quality management plans by accurately predicting the river water quality. Many researchers have utilized various tools for modelling the water quality of the river environment. Until now, river water quality has been modelled using the watershed model such as Soil and Water Assessment Tool (SWAT) [1], Hydrological Simulation Program-Fortran (HSPF) [2], and QUAL2E [3]. However, those models are developed in the US government (United States Department of Agriculture and United States Environmental Protection Agency), so it is challenging work to adapt those models to Korean watershed direct. And nowadays, the application of Artificial Intelligence (AI) is gradually increasing, because of its high prediction accuracy, adaptability for non-linearity, and high speed rather than other methodologies Despite the increasing use of AI in river water quality modelling, a challenge is that AI requires high-resolution dataset for effective modelling. However, in Korea, the resolution of the dataset for water quality of river environment is low because of lack of the number of conducted water quality monitoring stations.

This study is to identify cases and status of the system operation in the event of a disaster by investigating the operation and management systems for domestic and overseas disaster resources, and to design a prototype for efficient operation by deriving operation and management improvement measures. In order to analyze the status of systems for disaster resource operation and management, we investigated the system operation cases and status used for the operation and management of disaster resources in the event of a disaster. We then derived improvement measures for the operation and management of disaster resources through several cases in advanced countries. Based on these measures, users should be able to efficiently manage key disaster resources by disaster type. They will also be able to identify the disaster risk level by stage, and inquire organizations capable of support near disaster areas. If a disaster resource management prototype is developed through this study, it may be used in conjunction with the “digital-based integrated management system for disaster resources” project (2020-2022) supported by the Ministry of the Interior and Safety. It can be also used as a reference for establishing a disaster resource stockpile plan.

The numerical study was conducted to compare process performance depending on the pump type and process configuration. The daily monitoring data of seawater temperature and salinity offshore from Daesan, Republic of Korea was used to reflect the site-specific seawater conditions. An algorithm for reverse osmosis in constant permeate mode was developed to simulate the process in time-variant conditions. Two types of pumps with different maximum leachable efficiencies were employed to organize pump-train configuration: separated feed lines and common pressure center design. The results showed pump type and design configuration did not have a significant effect on process performance. The annual means of specific energy consumption (<i>SEC</i>) for every design configuration were under 2 kWh/m³, except for a worst-case. The worst-case was decided when the pump was operated out of the best operation range. The two operation strategies were evaluated to determine the optimal configuration. The permeate flow rate was reduced to 80% of the designed permeate flow rate with two approaches: feed flow rate reduction in every train and pump shutdown in a specific train. The operation mode with feed flow rate reduction was more efficient than the other. The operating pressure reduction led to a decrease in <i>SEC</i>.