• Smart Structures and Systems
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• v.16 no.5
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• pp.873-889
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• 2015

In civil engineering, probabilistic seismic risk assessment is used to predict the economic damage to a lifeline system of possible future earthquakes. The results are used to plan mitigation measures and to strengthen the structures where necessary. Instead, after an earthquake public authorities need mathematical models that compute: the damage caused by the earthquake to the individual vulnerable components and links, and the global behavior of the lifeline system. In this study, a framework that was developed and used for prediction purpose is modified to assess the consequences of an earthquake in quasi real-time after such earthquake happened. This is possible because nowadays entire seismic regions are instrumented with tight networks of strong motion stations, which provide and broadcast accurate intensity measure maps of the event to the public within minutes. The framework uses the broadcasted map and calculates the damage to the lifeline system and its component in quasi real-time. The results give the authorities the most likely status of the system. This helps emergency personnel to deal with the damage and to prioritize visual inspections and repairs. A highway transportation network is used as a test bed but any lifeline system can be analyzed.

• Journal of Environmental Science International
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• v.31 no.12
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• pp.1027-1038
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• 2022

The intertidal zone has both land and marine characteristics and shows complex weather environments. These characteristics are suited for studying climate change, energy balance and ecosystems, and may play an important role in coastal and marine weather prediction and analysis. This study was conducted at Odo Island, approximately 300m from the mainland in Yeosu. We built a weather observation system capable of real-time monitoring on the mud flat in the intertidal zone and measured actual weather and marine data. Weather observation was conducted from April to June 2022. The results showed changes in air temperature and water temperature with changes in the tide level during spring. Correlation analysis revealed characteristic changes in air temperature and water temperature during the day and night, and with inundation and exposure.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.

• Journal of Environmental Health Sciences
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• v.26 no.4
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• pp.134-140
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• 2000

The increase in traffic is a worldwide phenomenon. In Korea, it has been increased by 20% per annual in recent 1990’s and approximately 10 millions cars had been registered until 1997. This traffic could easily affect and contribute the local outdoor air quality(QAQ) concerned. The QAQ in highway in one of the examples and the subject in this study. The seoul tollgate located at the north-end of Keypngbu Highway was selected for the study. In case of highway tollgate, the local air pollution could be directly affected by the traffic to approach, wait and start the tollgate. Nitrogen dioxide (NO$_2$) concentration exceeded the EAQS(Environmental Air Quality Standards), but overall indoor air quality was a little better than the outdoor air quality. The measured TSP concentration was much higher than that of the EAQS. To apply a management to a air quality problem of Seoul tollgate, it was predicted air quality with traffic volume and weather condition. It was calculated NO$_2$ emission with traffic volume and predicted in and out of booth by CALINE3 at the Seoul tollgate. To make a comparison between measured and predicted concentration, the prediction was good. It was shown that NO$_2$ concentration was high in the morning at the from Seoul direction and in the evening at the to Seoul direction. it was thought that NO$_2$ concentration variation was reflected according to the traffic volume.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1873-1879
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• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.628-636
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• 2021

This paper contains the implementation and performance evaluation results of a system that collects environmental data at the time when the wild animal intrusion occurred at farms and then predicts future wild animal intrusions through a machine learning-based Vector Autoregression(VAR) model. To collect the data for intrusion prediction, an IoT-based hardware prototype was developed, which was installed on a small farm located near the school and simulated over a long period to generate intrusion events. The intrusion prediction service based on the implemented VAR model provides the date and time when intrusion is likely to occur over the next 30 days. In addition, the proposed system includes the function of providing real-time notifications to the farmers mobile device when wild animals intrusion occurs in the farm, and performance evaluation was conducted to confirm that the average response time was 7.89 seconds.

• Korean Journal of Ecology and Environment
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• v.46 no.1
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• pp.1-9
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• 2013

Algal blooms in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. In addition to precipitating taste and odor problems, blooms damage the environment, and some classes like cyanobacteria (blue-green algae) release toxins that can threaten human health, even causing death. There is a recognized need in the water industry for models that can accurately forecast in real-time algal bloom events for planning and mitigation purposes. In this study, using data for an interconnected system of rivers and reservoirs operated by a New Jersey water utility, various ANN models, including both discrete prediction and classification models, were developed and tested for forecasting counts of three different algal classes for one-week and two-weeks ahead periods. Predictor model inputs included physical, meteorological, chemical, and biological variables, and two different temporal schemes for processing inputs relative to the prediction event were used. Despite relatively limited historical data, the discrete prediction ANN models generally performed well during validation, achieving relatively high correlation coefficients, and often predicting the formation and dissipation of high algae count periods. The ANN classification models also performed well, with average classification percentages averaging 94 percent accuracy. Despite relatively limited data events, this study demonstrates that with adequate data collection, both in terms of the number of historical events and availability of important predictor variables, ANNs can provide accurate real-time forecasts of algal population counts, as well as foster increased understanding of important cause and effect relationships, which can be used to both improve monitoring programs and forecasting efforts.

• Journal of Korean Society on Water Environment
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• v.37 no.5
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• pp.344-354
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• 2021

Optical methods such as UV and fluorescence spectrophotometers can be applied not only in the qualitative analysis of dissolved organic matter (DOM), but also in real-time quantitative DOM monitoring for wastewater and natural water. In this study, we measure the UV₂₅₄ and fluorescence excitation emission spectra for a sewage treatment plant influent and effluent, and river water before and after sewage effluent flows into the river to examine the composition and origin of DOM. In addition, a correlation analysis between quantified DOM characteristics and dissolved organic carbon (DOC) was conducted. Based on the fluorescence excitation emission spectra analysis, it was confirmed that the protein-type tryptophan-like DOM was the dominant substance in the influent, and that the organic matter exhibited relatively more humic properties after biological treatment. However, DOM in river water showed the fluorescence characteristics of terrestrial humic-like and algal tyrosine-like (protein-like) organic matter. In addition, a correlation analysis was conducted between the DOC and optical indices such as UV₂₅₄, the fluorescence intensity of protein-like and humic-like organic matter, then DOC prediction models were suggested for wastewater and river monitoring during non-rainfall and rainfall events. This study provides basic information that can improve the understanding of the contribution of DOC concentration by DOM components, and can be used for organic carbon concentration management in wastewater and natural water.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.5
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• pp.671-683
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• 2018

The flood damage caused by heavy rains in urban watershed is increasing, and, as evidenced by many previous studies, urban flooding usually exceeds the water capacity of drainage networks. The flood on the area which considerably urbanized and densely populated cause serious social and economic damage. To solve this problem, deterministic and probabilistic studies have been conducted for the prediction flooding in urban areas. However, it is insufficient to obtain lead times and to derive the prediction results for the flood volume in a short period of time. In this study, IDNN, TDNN and NARX were compared for real-time flood prediction based on urban runoff analysis to present the optimal real-time urban flood prediction technique. As a result of the flood prediction with rainfall event of 2010 and 2011 in Gangnam area, the Nash efficiency coefficient of the input delay artificial neural network, the time delay neural network and nonlinear autoregressive network with exogenous inputs are 0.86, 0.92, 0.99 and 0.53, 0.41, 0.98 respectively. Comparing with the result of the error analysis on the predicted result, it is revealed that the use of nonlinear autoregressive network with exogenous inputs must be appropriate for the establishment of urban flood response system in the future.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.11a
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• pp.490-494
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• 2008

We simulated a forest fire which was occurred in Yangyang area on 2005 and compared a results between two different weather conditions(real weather condition and mean weather condition since 1968) using FARSITE, which is a forest fire spread simulator for preventing and predicting fire in USDA. And, we researched a problem in the transition for introducing, so we serve the basic method for prevention and attacking fire. In the result, severe weather condition on 2005 effected a forest fire behavior. The rate of spread under real weather condition was about 4 times faster than mean weather condition. Damaged area was about 10 time than mean weather condition. Therefore, Climate change will make a more sever fire season. As we will encounter to need for accurate prediction in near future, it will be necessary to predict a forest fire linked with future wether and fuel condition.