:In this study, we have investigated the water quality and flow rate at the 3 sites of main stream and 11 sites of the branch stream of Hwang River from January, 2007 to 2010, and analyzed the effects on Hwang River with the purpose of using the data for as the fundamental information for water quality improvement and water resource management in the water system of Hap-Cheon Lake Upper Stream. The flow rate at 3 sites of the main stream and 11 sites of the branch stream increased during the rainy season between June and September, and continuously decreased during the dry season starting from autumn to winter. The results of correlation analysis with Pearson correlation coefficient showed that BOD5 and CODMn, BOD5 and T-P, and CODMn and TSS at the 3 sites of the main stream had high correlation with each other. We have also analyzed the correlation between Chl-a and major factors at the 3 sites of the main stream. Chl-a and the water temperature Negative correlation coefficient and that of Chl-a and BOD5, CODMn Positive correlation coefficient showed. The N/P ratio at all the 3 sites of the main stream was higher than 16 by DIN/DIP and T-N/T-P, indicating that phosphorus is acting as the limited nutrient.
This study tried to investigate and analyze the actual state such as the regional, classified, and material characteristics of the water quality in order to research the several factors by which the filtrated water of the total 250 cases can be polluted in the water tank. The 215 points (86%) clean the water tanks twice a year regularly and J-city has done the best job of cleaning the water tanks. The fifty points (20%) of the total 250 investigation points examine the water quality of the water tanks every year, however, the 175 investigation points (70%) do not execute the inspection of water quality. In the case of the regional characteristics in the water quality, the 23 points (46%) in H-county, the 17 points (34%) in S-county, and the 16 points (32%) in G-city are incongruent in the standard, and the incongruity ratio of the water quality in J-city is the lowest. The result of the classified incongruity shows that total coliforms were found at the 61 investigation points, mesophilic bacteria were found at the 27 points, and turbidity was found at the 12 points. In the case of the material incongruity, concrete was found at the 63 investigation points as the most distinguished factor, and FRP (fiberglass reinforced plastic) at the 23 points, SMC (sheet molding compound) at the 12 points, and stainless steel was found at the 2 points.
a b s t r a c tAccurate screening of sewer conditions from monitoring data contributes to maintaining their operations (in terms of water quality and quantity) safe as well as reducing their associated costs (for operation and maintenance). This study was designed to assess the performance deterioration in sewer systems using a series of data classification tools, namely classical classification and novel supervised learning algorithms. The hydraulic data available for four sewer systems at Jinju City in Korea in a daily format during the monitoring period of 2013-2017 were provided as example data sets to those algorithms, which were evaluated independently with 70% training and 30% test data sets randomly divided. A self-organizing map (SOM) with a specialty in extracting hidden patterns in data was used to classify the data sets into three warning levels in the absence of any definite warning criteria for individual parameters. Our findings showed that three supervised learning algorithms achieved comparable performance in predicting warning levels defined from SOM to exiting classification algorithm in terms of accuracy and error rate. The network architecture optimized for supervised learning algorithms, in fact, varied significantly depending on the data sets, including that with additional variables on top of the original data set. In contrast, exiting classification algorithm unexpectedly produced high error rates in case that the hydraulic parameters had low coefficient of variation values reaching as high as 16%. Overall, these results demonstrated that novel supervised learning algorithms were more universally applicable for the assessment of hydraulic and/or water quality conditions in sewer systems than classical classification algorithm, regardless of the amount of variability in the data sets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.