• Journal of Environmental Science International
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• v.2 no.3
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• pp.193-199
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• 1993

This study has two objectives. One is developing the runoff model for Hoe-Dong Reservoir basin located at the upstream of Su-Young River in Pusan. To develop the runoff model, basic hydrological parameters - curve number to find effective rainfall, and storage coefficient, etc. - should be estimated. In this study, the effective rainfall was calculated by the SCS method, and the storage coefficient used in the Clark watershed routing was cited from the report of P.E.B. The other is the derivation of transfer function for Hoe-Dong Reservoir basin. The linear, discrete, input-output model which contained six parameters was selected, and the parameters were estimated by the least square method and the correlation function method, respectively. Throughout this study, rainfall and flood discharge data were based on the field observation in 1981.8.22 - 8.23 (typhoon Gladys). It was observed that the Clark watershed routing regenerated the flood hydrograph of typhoon Gladys very well, and this fact showed that the estimated hydrological parameters were relatively correct. Also, the calculated hydrograph by the linear, discrete, input-output model showed good agreement with the regenerated hydrograph at Hoe-Dong Dam site, so this model can be applicable to other small urban areas. Key Words : runoff, effective rainfall, SCS method, clark watershed iou상ng, hydrological parameters, parameter estimation, least square method, correlation function method, input-output model, typhoon gladys.

• Journal of Korea Water Resources Association
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• v.49 no.9
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• pp.789-798
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• 2016

Frequent algal blooms at major river systems in Korea have been serious social and environmental problems. Especially, the appearance of cyanobacteria with toxic materials is a threat to secure a safe drinking water. In order to model the behaviour of cyanobacteria cell number, an exclusive causality analysis using prewhitening technique was introduced to delineate effective parameters to predict the cell numbers of cyanobacteria in Seungchon Weir and Juksan Weir along Youngsan river system. Both input and output transfer function models were obtained to explain temporal variation of cyanobacteria cell number. A threshold behaviour of water temperature was implemented into the model development to consider winter characteristic of cyanobacteria. The implementation of water temperature threshold into the model structure improves the predictability in simulation. Even though the input output transfer model cannot completely explained all blooms of cyanobacteria, the simple structure of model provide a feasibility in application which can be important in practical aspect.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Economic and Environmental Geology
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• v.47 no.3
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• pp.237-246
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• 2014

In this study, it is estimated how many changes of macroeconomic variables are happened under the proposition of import substitution of mining products 1% using macroeconometric input-output model. For this, used macroeconometric input-output model is composed of 141 behavioral equations representing the macroeconomy structure. In general, macroeconometrics models are constructed mainly on the side of the expenditure then it is not easy to estimate the effects of the shocks occurred from industry level. To mitigate that, this study tries to construct a macroeconometric input-output model. Macroeconometrics model which is useful to estimate the effects of macroeconomic shocks, economic policy and more, in this study, is linked with input-output table through the NDI(national disposable income) derived from compensation of employee. And this paper presents the estimation results of import substitution effects of mining products on Korean economy. As a results, GDP is increased 0.00073%, gross labor employed 0.00029%, current balanace 0.00010% and unemployment rate is mitigated 0.00233%.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.14 no.4
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• pp.859-866
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• 1994

It is difficult to make an exact estimate of the peak discharge or the runoff depth of flood and establish the proper measure for the flood protection since the water stage or discharge has been nearly measured at most medium or small river basins. The objective of this study is to estimate parameters of the discrete, linear, input-output model for medium or small river basin. The On-Cheon River basin in Pusan was selected for the study area. The runoff data used in the study has been observed since June 1993, and the effective rainfall was determined using the storage function method. The parameter sets of the discrete, linear, input-output model were estimated using the least squares method and the correlation function method, respectively. The calculated hydrographs by the discrete, linear, input-output model regenerated the observed outflow hydrographs well, and also the simulated flood hydrograph was comparable to the observed one. Therefore, it is believed that the discrete, linear, input-output model is simpler than other runoff analysis methods, and can be applied to a medium or small river basin.

• Environmental and Resource Economics Review
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• v.14 no.4
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• pp.839-866
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• 2005

This study addresses the effects on the economy of atomic sector's R&D by considering how much KAERI's R&D expenditures in 2000 affect on the production and value-added of each industry and the whole economy. This study answers to the question using competitive import input-output tables and both supply driven and demand driven IO models, which are frequently employed in evaluating economic impacts of R&D in both domestic and foreign academic areas.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.46
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• pp.207-219
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• 1998

Multiple Objective Programming(MOP) has been suggested for the solution of completed decision problems. Decision analysis in numerous areas, including energy and environmental planning, necessarily requires consideration of multiple conflicting objectives, MOP has been successfully applied to a number of these problems. The objective of this paper is to present a MOP process which are integrated model with the Input-Output(I-O) analysis for energy and environment planning in industrial sectors. In the model, three objectives are observed such as (1) value added (2) total energy consumption and (3) environmental impacts. Special emphasis is placed on the police implications of industrial structures.

• Smart Structures and Systems
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• v.24 no.5
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.786-789
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• 2002

A recurrent artificial neural network (ANN) model is developed to simulate hydrocarbon recovery process at petroleum-contaminated site. The groundwater extraction rate, vacuum pressure, and saturation hydraulic conductivity are selected as the input variables, while the cumulative hydrocarbon recovery volume is considered as the output variable. The experimental data fer establishing the ANN model are from implementation of a multiphase flow model for dual phase remediation process under different input variable conditions. The complex nonlinear and dynamic relationship between input and output data sets are then identified through the developed ANN model. Reasonable agreements between modeling results and experimental data are observed, which reveals high effectiveness and efficiency of the neural network approach in modeling complex hydrocarbon recovery behavior.