Moo-Jong Park

Development of Optimal Decision-Making System for Rehabilitation of Water Distribution Systems Using ReHS

The study on the plan for rehabilitation project of domestic water distribution system - especially using Heuristic Algorithm as Genetic Algorithm which is expected to provide a more optimal solution effectively - has not been done sufficiently. The purpose of this study is the development of the optimal decision making system for the rehabilitation of the water distribution system considering economic and hydraulic influences using ReHS which is recent study of OR technique. Five different models with different objective functions are developed and tested to virtual pipe network according to various conditions considered in this study. These models provide more options for the rehabilitation of pipe network systems compared to previously suggested models in the literature.

Flash Flood Risk Assessment using PROMETHEE and Entropy Method

Previously most of flood prevention efforts have been made for relatively large watersheds near to channel flow. However, as economical development and the expansion of leisure areas to mountainous region, human casualty by flash flood occurs frequently, requiring additional prevention activity. Therefore, to reduce the damage of human lives and property by flash flood, we develop an assessment method for flash flood occurrence for mountainous areas considering various factors involving it. PROMETHEE(Preference Ranking Organization METHod for Enrichment Evaluations) which is one of the MCDM(Multi-Criteria Decision Making) was adopted to assess the contribution of each factor to the risk of the flash flood in the mountainous area. The main evaluation criteria are classified into three categories, namely, the regional and rainfall characteristics, and geographical features. Also, the Entropy method is used to determine the weight of each evaluation criteria without survey. The suggested method based on PROMETHEE with Entropy method is applied to BongHwa region to verify its applicability. After applied, the method successfully assesses the relative risk of flash flood occurrence of each sub region in the BongHwa region. Out of the seventeen sub-regions, five, seven and five of them are evaluated as high-risk, medium-risk, and low-risk, respectively. To verify the results, we searched the historical data of flash flood and the flash flood had occurred in one of high-risk sub-regions at 2008.

An Evaluation of Inundation Risk of Urban Watershed Using Data Envelopment Analysis

Abstract In order to prevent the flood damage of urban watersheds, it is important to anticipate the relative inundation risk of urbanwatersheds and to perform inundation damage mitigation projects. Therefore, we propose a methodology for evaluation of the rel-ative inundation risk of urban watersheds to determine the project priority for each watershed. For this purpose, we suggest to usecross efficiency of Data Envelopment Analysis (DEA), which can consider the probability of inundation occurrence and the inun-dation damage simultaneously. In this method, the average slope, density of conduit, elevation, and the sediment yield per unitarea of subcatchment were used as input parameters representing the probability of inundation occurrence while population of thesubcatchment as an output representing the inundation damage. After the suggested method is applied to GunJan Basin, Seoul, it isshown that the DEA model can estimate the relative inundation risk of each watershed even though it does not require decisionmakers to asses the weight on individual input and output parameter, which is required in the other multi-criteria decision makingmodels such as the Analytic Hierarchy Process and PROMETHEE. The estimated relative inundation risk of each watershed canbe used to determine the project priority for each watershed. Key words : Decision making, Cross efficiency, Data envelopment analysis, Relative inundation risk, Urban watershed

Method to Determinate Monitoring Points in Sewer Networks

In order to manage a sewer system effectively, flow conditions such as flux, water quality, Infiltration and Inflow (I/I), Combined Sewer Overflows (CSOs), etc need to be monitored on a regular base. Therefore, in sewer networks, a monitoring is so important to prevent the river disaster. Monitoring all nodes of an entire sewer system is not necessary and cost-prohibitive. Water quality monitoring points that can represent a sewer system should be selected in a economical manner. There is no a standard for the selection of monitoring points and the quantitative analysis of the observed data has not been applied in sewer system. In this study, the entropy method was applied for a sewer network to evaluate and determine the optimal water quality monitoring points using genetic algorithm. The entropy method allows to analyze the observed data for the pattern and magnitude of temporal water quality change. Since water quality measurement usually accompanies with flow measurement, a set of installation locations of flowmeters was chosen as decision variables in this study.

Study for Management of Non-point Source in Sooyeong River Watershed Using SWMM

비점오염원은 강우 유출수와 함께 유출되는 특성을 지니고 있어, 유역에서의 강우-유출 현상 및 이에 영향을 미치는 토 지이용, 지형 및 지질, 유역의 형상, 강우특성 등을 고려한 관 리 전략 수립이 필수적이다. 일반적으로 도시지역은 불투수성 면적 비율이 높아 지표면 유출량과 첨두유량 증가(Jennings and Jarnagin, 2002; Lepold, 1968), 지하수함량의 감소, 용천 수 고갈 등에 의한 하천 건천화를 초래하게 된다(Ferguson and Suckling, 1990; Wang et al., 2001). 자연유역과는 다른 수문반응을 보이는 이러한 도시지역에서의 수문현상은 평상 시 하천 건천화, 홍수시 내수배제 불량으로 인한 침수 및 범 람, 비점오염원 및 합류식 하수도 월류수로 인한 수질 및 수 생태계 건강성 저하 등의 문제점을 유발하고 있는 실정이다. 따라서 비점오염원 관리 특히, 도시지역에서의 비점오염원 의 관리는 이러한 포괄적인 문제점에 대한 대안모색 즉, 평상 시 기저유량 확보를 통한 유지용수 확보, 홍수 저감기능, 비점 오염원의 제어를 포함한 방류하천의 근본적인 수질개선에 대 한 종합적이고 현실적인 접근 전략이 요구되고 있다. 비점오염원 관리에 대한 국내 연구를 살펴보면 Choi(2003) 는 초기강우 유출수에 포함되어 있는 입자상 오염물질을 처 리하기 위한 시설을 연구한 바 있으며, 주택단지와 산업단지 Abstract

Gilanchon Watershed Water Quality Simulation Using QUALKO & QUAL2K

This study has implemented QUALKO and QUAL2K models for the Nakdong River watershed, which can simulate the change in organic matters due to growth and death of algae. Also the two models simulated three water quality factors such as BOD, T-N, and T-P to understand characteristics of the both models. The model errors, differences between observed and simulated values, result in ranges of 25.99%-26.57% for BOD, 2.34%-3.29% for T-N, and 3.57%-7.14% for T-P, respectively. The model error for BOD is relatively larger because it is simulated by the meling and hydrolysis of Detritus. The simulated result of T-N was more accurate in QUALKO than QUALK2K. The trend of T-P increases from upstream to downstream due to increases of organic P and dissolved P from point sources along the river. In general, the results from QUALKO and QUAL2K models were similar and the model errors from the both models produce no significant differences. The calibration of parameters in models is required to improve model predictability.