• Journal of the Korean GEO-environmental Society
• /
• v.3 no.4
• /
• pp.11-17
• /
• 2002

The purpose of this study is the prediction of pollutant loads into Seomjin river watershed using neural networks model. The pollutant loads into river watershed depend upon the water quantity of inflow from the upstream as well as the water quality of the inflow into the river. For the estimation of pollutants into river, a neural networks model which has the features of multi-layered structure and parallel multi-connections is used. The used water quality parameters are BOD, COD and SS into Seomjin river. The results of calibration are satisfactory, and proved the availability of a proposed neural networks model to estimate short-term water quality pollutants into river system.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.2193-2196
• /
• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• Korean Journal of Ecology and Environment
• /
• v.45 no.2
• /
• pp.150-157
• /
• 2012

Fish communities in river networks provide significant ecosystem services that will likely decline under future land use and climate change. We developed a model that simulates the consequences to multiple populations of one or more fish species-a meta-community-from multiple stressors across a river network. The model is spatially-explicit and age-structured, with three components: habitat suitability; population dynamics, including species interactions; and movement across a spatial network. Although this model is simple, it can form the basis of fisheries assessments and may be incorporated into an integrated modeling system for watershed management and prediction.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.2
• /
• pp.88-99
• /
• 2022

The river network is one of the essential topographical characteristics in river management. The river network which as previously constructed by the ground surveying method has recently begun to be efficiently constructed using the remote sensing datasets. Since it is difficult to remove these obstacles such as bridges in the urban rivers, it is rare to construct the urban river networks with the various obstacles. In this study, the Sentinel-2 satellite imagery was used to develop the automatic method for detecting the urban river networks without the obstacles and with the preserved boundaries as follows. First, the normalized difference water index image was generated using the multispectral bands of the given Sentinel-2 satellite imagery, and the binary image that could classify the water body and other regions was generated. Next, the morphological operations were employed for detecting the complete river networks with the obstacles removed and the boundaries preserved. As a result of applying the proposed methodology to Han River in Seoul, the complete river networks with the obstacles removed and the boundaries preserved were well constructed.

• 한국방재학회:학술대회논문집
• /
• 2008.02a
• /
• pp.669-673
• /
• 2008

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.1427-1431
• /
• 2007

In this study, the new methodology which combines Kohonen self-organizing map(KSOM) neural networks model and the conventional neural networks models such as feedforward neural networks model and generalized neural networks model is introduced to forecast flood stage in Nakdong river, Republic of Korea. It is possible to train without output data in KSOM neural networks model. KSOM neural networks model is used to classify the input data before it combines with the conventional neural networks model. Four types of models such as SOM-FFNNM-BP, SOM-GRNNM-GA, FFNNM-BP, and GRNNM-GA are used to train and test performances respectively. From the statistical analysis for training and testing performances, SOM-GRNNM-GA shows the best results compared with the other models such as SOM-FFNNM-BP, FFNNM-BP, and GRNNM-GA and FFNNM-BP shows vice-versa. From this study, we can suggest the new methodology to forecast flood stage and construct flood warning system in river basin.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.159-159
• /
• 2023

The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

• Korean Journal of Hydrosciences
• /
• v.2
• /
• pp.85-98
• /
• 1991

This paper is focused on the development of the RIVNET1 model, which is a dynamic wave model, for flood analysis in dendritic river networks with arbitrary cross-sections. This model adopted the $-point implicit RDM and utilized a relaxation algorithim in order to solve the governing equations. The double-sweep method was used to reduce the C.P.U. time to solve the matrix system of the model. This model is applied the analyze flood waves of the Ohid river in the U.S.A. and the Keum river in Korea. The results of analysis obtained from this model are compared with those of the DWOPER and observed data.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.259-259
• /
• 2016

This study examined Artificial Neurons Networks model (ANNs) for forecast flash discharge at Southern part of Thailand by using rainfall data and discharge data. The Sungai Kolok River Basin has meant the border crossing between Thailand and Malaysia which watershed drains an area lies in Thailand 691.88 square kilometer from over all 2,175 square kilometer. The river originates in mountainous area of Waeng district then flow through Gulf of Thailand at Narathiwat Province, which the river length is approximately 103 kilometers. Almost every year, flooding seems to have increased in frequency and magnitude which is highly non-linear and complicated phenomena. The purpose of this study is to forecast runoff on Sungai Kolok at X.119A gauge station (Sungai Kolok district, Narathiwat province) for 3 days in advance by using Artificial Neural Networks model (ANNs). 3 daily rainfall stations and 2 daily runoff station have been measured by Royal Irrigation Department and Meteorological Department during flood period 2000-2014 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Coefficient of determination ($R^2$). The result of the first day gets the highest accuracy and then decreased in day 2 and day 3, consequently. $R^2$values for first day, second day and third day of runoff forecasting is 0.71, 0.62 and 0.49 respectively. The results confirmed that the ANNs model can be used when the range of collected dataset is short and real-time operated. In conclusion, the ANNs model is suitable to runoff forecasting during flood incident of Sungai Kolok river because it is straightforward model and require with only a few parameters for simulation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.142-142
• /
• 2023

Humans inevitably and continuously produce wastewater in daily life worldwide. To decrease the degradation of river water bodies and aquatic ecosystem therein, humans have built systems at different scales to collect, drain, and treat household-produced wastewater. Particularly, municipal wastewater treatment plants (WWTPs) with centralized controls have played a key role in reducing loads of nutrients in domestic wastewater for the last few decades. Notwithstanding such contributions, impaired rivers regarding water quality and habitat integrity still exist at the whole river basin scale. It is highly attributable to the absence of dilution capacity of receiving streams and/or the accumulation of the pollutant loads along flow paths. To improve the perspective for individual WWTPs assessment, the first crucial step is to achieve systematic understanding on spatial distribution characteristics of all WWTPs together in a given river basin. By taking the initiative, our former study showed spatial hierarchical distributions of WWTPs in three large urbanized river basins in Germany. In this study, we uncover how municipal WWTPs in the contiguous United States are distributed along river networks in a give river basin. The extended spatial scope allows to deal with wide ranges in geomorphological attributes, hydro-climatic conditions, and socio-economic status. Furthermore, we identify the relation of the findings with multiple factors related to human activities, such as the spatial distribution of human settlements, the degree of economy development, and the fraction of communities served by WWTPs. Generalizable patterns found in this study are expected to contribute to establishing viable management plans for recent water-environmental challenges caused by WWTP-discharges to river water bodies.