This study has been conducted to investigate the North Korea water supply system. For this purpose, laws and regulations concerning the North Korea water utilities were analyzed. According to this study, the water supply system of North Korea is similar to that of South Korea. The major difference between these two systems lies in the national water supplier: South Korea has the national water supplier as well as the municipal suppliers, while there is no national water supplier in North Korea. It is noted that the North Korean water utilities depend on outside suppliers for resources necessary for water production such as electricity, chemicals, spare parts, etc. This could damage the North Korean water utilities. If required resources were not delivered properly (deficient quantity and/or at delayed timing), the water suppliers would encounter difficulties in water production.
In this study, we propose a flow velocity evaluation scheme based on pressure measurement in pressurized pipeline systems. Conservation of mass and momentum equations can be decomposed into mean and perturbation of pressure head and flowrate, which provide the pressure head and flowrate relationship between upstream and donwstream point in pressurized pipeline system. The inverse impedance formulations were derived to address measured pressure at downstream to evaluation of flow velocity or pressure at any point of system. The convolution of response function to pressure head in downstream valve provides the flow velocity response in any point of the simple pipeline system. Simulation comparison between traditional method of characteristics and the proposed method provide good agreements between two distinct approaches.
The sewer capacity design have been based on the Huff model or the rational equation in South Korea and often failed to determine optimal capacity, resulting in frequent urban flooding or over-sizing. A time distribution of rainfall (i.e., Huff or ABM method) could be used instead of a rainfall hyetograph obtained from statistical analysis of previous rainfalls. In this study, the Huff method and the ABM method, which predict the time distribution of rain intensity, which are widely used to calculate sewage pipe drainage capacity using the SWMM, were compared with the standard rainfall intensity hyetograph of Seoul. If the rainfall duration was 30 minutes to 180 minutes, the rainfall intensity value calculated by the Huff model tended to be less than the rainfall intensity value of the standard rainfall intensity in the initial 5-10 minutes. As a result, more than 10% to 30% of under-design would be made. In addition, the rainfall intensity value calculated by the Huff model from the section excluding the initial 5-10 minutes of rainfall to the rainfall duration was calculated larger than the value using the standard rainfall intensity equation, which would result in an over-design of 10% to 30%. In the case of a relatively long rainfall duration of 360 minutes (6 hours) to 1,440 minutes (24 hours), it showed an lower rainfall intensity of 60 to 90% in the early stages of rainfall, but the problem of under-design had been solved as the rainfall duration time had elapsed. On the other hand, in the alternating block method (ABM) method, it was found that the rainfall intensity at the entire period at each assumed rainfall duration accurately matched the standard rainfall intensity hyetograph of Seoul.
The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.