Objectives of this study were to identify the hotspot for displacement of the on-line water quality sensors, in order to detect illicit discharge of untreated wastewater. A total of twenty-six water quality parameters were measured in sewer networks of the industrial complex located in Daejeon city as a test-bed site of this study. For the water qualities measured on a daily basis by 2-hour interval, the self-organizing maps(SOMs), one of the artificial neural networks(ANNs), were applied to classify the catchments to the clusters in accordance with patterns of water qualities discharged, and to determine the hotspot for priority sensor allocation in the study. The results revealed that the catchments were classified into four clusters in terms of extent of water qualities, in which the grouping were validated by the Euclidean distance and Davies-Bouldin index. Of the on-line sensors, total organic carbon(TOC) sensor, selected to be suitable for organic pollutants monitoring, would be effective to be allocated in D and a part of E catchments. Pb sensor, of heavy metals, would be suitable to be displaced in A and a part of B catchments.

This study was performed to propose the sewer defect scoring, and grading protocols for sewer condition assessment. For this, sewer defect scoring methods were comparatively analyzed and reviewed for four international condition assessment protocols, which are established based on WRc manual. As a result, we proposed a new protocol for sewer condition assessment, in which characteristics of sewer pipes are considered by segment. In reference to the PIM-3, the extent of ground subsidence was adopted to be of importance, and renewal scores increased in accordance with weighting of defects causing structural backfill materials. Also, defect grades of ‘Hole’ were extended to 5 levels of the grading, and ‘Surface Damage’ was excluded in defect assessment. The addition of ‘Buckling’ resulted in reduction of weights in ‘Surface Damage’ and ‘Lining Defects’.

Sewer condition assessment involves the determination of defective points and status of aged sewers by a CCTV inspection according to the standard manual. Therefore, it is important to establish a reliable and effective standard manual for identifying the sewer defect. In this study, analytic reviews of the CCTV inspection manuals of the UK, New Zealand, Canada and South Korea were performed in order to compare the defect codes and the protocols of condition assessment. Through this, we also established the standardized method for defect code and revised the calculation method of assigning the condition grade. Analyses of the types and frequencies of sewer defects that obtained by CCTV inspection of 7000 case results, showed that the joint defect and lateral defect were the most frequent defects that occurred in Korea. Some defect codes are found to be modified because those did not occur at all. This study includes a proposed new sewer defect codes based on sewer characteristics.

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

The Baermann funnel method requires three to four Kimwipes tissues for clean nematode extraction. To find more efficient and economical nematode extraction paper, 14 different kinds of tissue papers were tested and compared with Kimwipes tissues. Nematode species used in the extraction efficiency test were juvenile (J2) of Heterodera spp., J2 of Meloidogyne spp., Pratylenchus spp., Rhabditis spp., Acrobeloides spp., Panagrolaimus spp., Poikilolaimus spp. and Diplogasterida. Extraction efficiency varied between 42.0 to 88.8%. Considering costs, extraction efficacy, and cleanness, one sheet of Pulling Kitchen Towel (MONALISA Co., Korea) is selected.

Halo merger trees are the essential backbone of semi-analytic models for galaxy formation and evolution. Srisawat et al. (2013) show that different tree building algorithms can build different halo merger histories from a numerical simulation for structure formation. In order to understand the differences induced by various tree building algorithms, we investigate the impact of halo merger trees on a semi-analytic model. We find that galaxy properties in our models show differences between trees when using a common parameter set. The models independently calibrated for each tree can reduce the discrepancies between global galaxy properties at z=0. Conversely, with regard to the evolutionary features of galaxies, the calibration slightly increases the differences between trees. Therefore, halo merger trees extracted from a common numerical simulation using different, but reliable, algorithms can result in different galaxy properties in the semi-analytic model. Considering the uncertainties in baryonic physics governing galaxy formation and evolution, however, these differences may not necessarily be significant.

Developing two process models to simulate wastewater treatment process is needed to draw a comparison between measured BOD data and estimated process model data: a mathematical model based on the process mass-balance and an ANN (artificial neural network) model. Those two types of simulator can fit well in terms of effluent BOD data, which models are formulated based on the distinctive five parameters: influent flow rate, effluent flow rate, influent BOD concentration, biomass concentration, and returned sludge percentage. The structuralized mass-balance model and ANN modeI with seasonal periods can estimate data set more precisely, and changing optimization algorithm for the penalty could be a useful option to tune up the process behavior estimations. An complex model such as ANN model coupled with mass-balance equation will be required to simulate process dynamics more accurately.