• 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.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.4
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• pp.75-83
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• 2007

Urbanized area has complex terrain with many flow paths. Almost stormwater is drained through pipe network because most area is impervious. And overland flow from the pipe network reform the surface flow. Therefore, it should be considered the drainage system and surface runoff both in urban inundation analysis. It is analyzed by using MIKE FLOOD integrated 1 dimension - 2 dimension model about Incheon Gyo urbanized watershed and compared with the results of 1 dimension model and 2 dimension model. At the result this approach linking of 2 dimension and 1 dimension pipe hydraulic model in MIKE FLOOD give accuracy that offers substantial improvement over earlier approach and more information about inundation such as water dapth, velocity or risk of flood, because it is possible to present storage of overland flow and topographical characteristic of area.

• Journal of the Korean Geographical Society
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• v.39 no.4
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• pp.633-642
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• 2004

This study tries to generalize the stream network by constructing rule-based modelling. A study on the map generalization tends to be concentrated on development of algorithms for modification of linear features and evaluations to the limited cartographic elements. Rule-based modelling can help to improve previous algorithms by application of generalization process with the results that analyzing mapping principles and spatial distribution patterns of geographical phenomena. Rule-based modelling can be applied to generalize various cartographic elements, and make an effective on multi-scaling mapping in the digital environments. In this research, nile-based modelling for stream network is composed of generalization rule, algorithm for centerline extraction and linear features. Before generalization, drainage pattern was analyzed by the connectivity with lake to minimize logical errors. As a result, 17 streams with centerline are extracted from 108 double-lined streams. Total length of stream networks is reduced as 17% in 1:25,000 scale, and as 29% in 1:50,000. Simoo algorithm, which is developed to generalize linear features, is compared to Douglas-Peucker(D-P) algorithm. D-P made linear features rough due to the increase of data point distance and widening of external angle. But in Simoo, linear features are smoothed with the decrease of scale.

• Economic and Environmental Geology
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• v.35 no.1
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• pp.67-74
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• 2002

The purpose of this study is to determine the weights of the factors for landslide susceptibility analysis using artificial neural network. Landslide locations were identified from interpretation of aerial photographs, field survey data, and topography. The landslide-related factors such as topographic slope, topographic curvature, soil drainage, soil effective thickness, soil texture, wood age and wood diameter were extracted from the spatial database in study area, Yongin. Using these factors, the weights of neural networks were calculated by backpropagation training algorithm and were used to determine the weight of landslide factors. Therefore, by interpreting the weights after training, the weight of each landslide factor can be ranked based on its contribution to the classification. The highest weight is topographic slope that is 5.33 and topographic curvature and soil texture are 1 and 1.17, respectively. Weight determination using backprogpagation algorithms can be used for overlay analysis of GIS so the factor that have low weight can be excluded in future analysis to save computation time.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.101-112
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• 2013

Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) offers opportunities to make advances in many research areas including hydrology by providing near-global scale elevation measurements at a uniform resolution. Its wide coverage and complimentary online access especially benefits researchers requiring topographic information of hard-to-access areas. However, SRTM DEM also contains inherent errors, which are subject to propagation with its manipulation into analysis outputs. Sensitivity of hydrologic analysis to the errors has not been fully understood yet. This study investigated their impact on estimation of hydrologic derivatives such as slope, stream network, and watershed boundary using Monte Carlo simulation and spatial moving average techniques. Different amount of the errors and their spatial auto-correlation structure were considered in the study. Two sub-watersheds of Geum and Deadong River areas located in South and North Korea, respectively, were selected as the study areas. The results demonstrated that the spatial presentations of stream networks and watershed boundaries and their length and area estimations could be greatly affected by the SRTM DEM errors, in particular relatively flat areas. In the Deadong River area, artifacts of the SRTM DEM created sinks even after the filling process and then closed drainage basin and short stream lines, which are not the case in the reality. These findings provided an evidence that SRTM DEM alone may not enough to accurately figure out the hydrologic feature of a watershed, suggesting need of local knowledge and complementary data.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.

• Journal of The Geomorphological Association of Korea
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• v.28 no.2
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• pp.71-85
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• 2021

In this study, the effect on the spatial distribution and diffusion of Arishigona, Sanaji and Bang-gae appearing in Jeolla-do was analyzed using geomorphic elements. Based on result, the AriShigona is distributed in the western plains of the Yeongsan River basin and around from the Noryeong mountain range to Mudeung mountain range, the Sanaji is mainly diffused in the middle and upper parts of the Seomjin River and the lower parts of the Mangyeong River, Dongjin River and the Boseong River basin, and the Bang-gae is found to be distributed in the Seomjin River and the upper part of the Yeongsan River basin. Although the cultural centers of these Rice-paddy Weeding Songs are different but they appear to have a similar distribution pattern in Jeolla-do. This is used as a diffusion path of cultural elements by crossing lineaments in various directions and serving bridge role at the same time. However, in the region where the lineaments do not intersect, the continuity of Rice-paddy Weeding Songs are relatively low, which are considered to be reflected in the spatial distribution and propagation of the sound due to the influence of the drain network rather than the lineament. The results of this study can provide basic data for spatial distribution of Rice-paddy Weeding Songs, and regionality and cultural division by diffusion characteristics.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.6
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• pp.1-13
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• 2023

Irrigation return flow plays an important role in river flow forecasting, basin water supply planning, and determining irrigation water use. Therefore, accurate calculation of irrigation return flow rate is essential for the rational use and management of water resources. In this study, EPA-SWMM (Environmental Protection Agency-Storm Water Management Model) modeling was used to analyze the irrigation return flow and return flow rate of each intake work using irrigation canal network. As a result of the EPA-SWMM, we tried to estimate the quick return flow and delayed return flow using the water supply, paddy field, drainage, infiltration, precipitation, and evapotranspiration. We selected 9 districts, including pumping stations and weirs, to reflect various characteristics of irrigation water, focusing on the four major rivers (Hangang, Geumgang, Nakdonggang, Yeongsangang, and Seomjingang). We analyzed the irrigation period from May 1, 2021 to September 10, 2021. As a result of estimating the irrigation return flow rate, it varied from approximately 44 to 56%. In the case of the Gokseong Guseong area with the highest return flow rate, it was estimated that the quick return flow was 4,677 10³ m³ and the delayed return flow was 1,473 10³ m³ , with a quick return flow rate of 42.6% and a delayed return flow rate of 13.4%.

• Environmental Engineering Research
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• v.23 no.1
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• pp.84-91
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• 2018

The present study deals with the management of groundwater resources of an important agriculture track of north-western part of Saudi Arabia. Due to strategic importance of the area efforts have been made to estimate aquifer proneness to attenuate contamination. This includes determining hydrodynamic behavior of the groundwater system. The important parameters of any vulnerability model are geological formations in the region, depth to water levels, soil, rainfall, topography, vadose zone, the drainage network and hydraulic conductivity, land use, hydrochemical data, water discharge, etc. All these parameters have greater control and helps determining response of groundwater system to a possible contaminant threat. A widely used DRASTIC model helps integrate these data layers to estimate vulnerability indices using GIS environment. DRASTIC parameters were assigned appropriate ratings depending upon existing data range and a constant weight factor. Further, land-use pattern map of study area was integrated with vulnerability map to produce pollution risk map. A comparison of DRASTIC model was done with GOD and AVI vulnerability models. Model validation was done with $NO_3$, $SO_4$ and Cl concentrations. These maps help to assess the zones of potential risk of contamination to the groundwater resources.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.2 no.1
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• pp.94-102
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• 1999

The retaining wall is a structure which was made for changing land form in many construction. The first role of the retaining wall is to maintain the slope stability. But recently, the amount of retaining wall have been increasing because of the expansion of construction works and the amenity of urban environment have been decreasing because of environmental destruction and the scenic heterogeneity. So we should consider the slope stability and ecological stability at the same time. The purpose of this study is to develop the retaining wall revegetation technology using the Eco-Stone, the structure of co-satisfying which included the slope stability and the revegetation effect. Eco-Stone is a structure which has high stability for earth pressure, settlement and drainage. And cost and term of construction works also have been decreased. Eco-Stone structure is one of factors composing the ecological network which is harmonize with surrounding environment. In this way, it is expected that the ecological habitats of various species would be restored.