• Environmental Engineering Research
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• 제21권4호
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• pp.333-340
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• 2016

An Artificial Neural Network including a Radial Basis Function (RBF) and a Time Delay Neural Network (TDNN) was used to predict total dissolved solid (TDS) in the river Zayanderud. Water quality parameters in the river for ten years, 2001-2010, were prepared from data monitored by the Isfahan Regional Water Authority. A factor analysis was applied to select the inputs of water quality parameters, which obtained total hardness, bicarbonate, chloride and calcium. Input data to the neural networks were pH, $Na^+$, $Mg^{2+}$, Carbonate ($CO{_3}^{-2}$), $HCO{_3}^{-1}$, $Cl^-$, $Ca^{2+}$ and Total hardness. For learning process 5-fold cross validation were applied. In the best situation, the TDNN contained 2 hidden layers of 15 neurons in each of the layers and the RBF had one hidden layer with 100 neurons. The Mean Squared Error and the Mean Bias Error for the TDNN during the training process were 0.0006 and 0.0603 and for the RBF neural network the mentioned errors were 0.0001 and 0.0006, respectively. In the RBF, the coefficient of determination ($R^2$) and the index of agreement (IA) between the observed data and predicted data were 0.997 and 0.999, respectively. In the TDNN, the $R^2$ and the IA between the actual and predicted data were 0.957 and 0.985, respectively. The results of sensitivity illustrated that $Ca^{2+}$ and $SO{_4}^{2-}$ parameters had the highest effect on the TDS prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• 제26권2B호
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• pp.145-152
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• 2006

In the present study, a nonlinear model of rainfall-runoff process using Artficial Neural networks(ANNs) which have no consideration on the physical parameter for the basin was developed at Naju station which is the main stream of Yeongsan-river, and Sunam station which is the main stream of Hwangryong-river. The result from the model of ANN_NJ_9 at the Naju station revealed the best result of the rainfall-runoff process, while the model of ANN_SA_9 for the Sunam station. Also, GUI_FFS developed in the research showed the $R^2$ of more than 0.98 between the observed and predicted values using the rainfall and runoff in the respective stations. Therefore, the GUI_FFS might be expected that it can play a role for the high reliability to operate and manage the water resources and the design of river plan more efficiently in the future.

• The Journal of the Korea institute of electronic communication sciences
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• 제13권4호
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• pp.787-794
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• 2018

The various natural disasters such as floods and localized heavy rains are increasing due to the global warming. If a natural disaster can be detected and analyzed in advance and more effectively, it can prevent enormous damage of natural disasters. Recent development in visual sensor technologies has encouraged various studies on monitoring environments including rivers. In this paper, we propose a method to detect water regions from river images which can be exploited for river surveillance systems using video sensor networks. In the proposed method, we first segment a river image finely using the minimum spanning tree algorithm. Then, the seed regions for the river region and the background region are set by using the preliminary information, and each seed region is expanded by merging similar regions to segment the water region from the image. Experimental results show that the proposed method separates the water region from a river image easier and accurately.

• Journal of Wetlands Research
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• 제15권3호
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• pp.397-406
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• 2013

Wetland conservation plan has been established to protect ecologically important wetlands based on vegetation integrity, spatial distribution of endangered species, but recently more demands are concentrated on the landscape ecological approaches such as topological relationship, neighboring area, spatial arrangements between wetlands at the broad scale. Landscape ecological analysis and graph theory are conducted to identify spatial characteristics related to core nodes and weak links of wetland networks in Nakdong basin. Regular planar model, which is selected for wetland networks, is applied in the Nakdong basin. The analysis indicates that 5 regional groups and 4 core wetlands are extracted with 15km threshold distance. The IIC and PC values based on the binary and probability models suggest that the wetland group C composed of main stream of Nakdong river and Geumho river is the most important area for wetland network. Wetland conservation plan, restoration projected of damaged and weak links between wetlands should be proposed through evaluating the node, links, and networks from wetlands at the local to the regional scale in Nakdong basin.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2015년도 학술발표회
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• pp.398-398
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• 2015

To estimate and forecast runoff by using Aritifitial Neaural Networks model (ANNs). it has been studied in Thailand for the past 10 years. The model was developed in order to be conformed with the conditions in which the collected dataset is short and the amount of dataset is inadequate. Every year, the Northerpart of Thailand faces river overflow and flood inundation. The most important basin in this area is Yom basin. The purpose of this study is to forecast runoff at Y.14 gauge station (Si-Satchanalai district, Sukhothai province) for 3 days in advance. This station located at the upstream area of Yom River basin. Daily rainfall and daily runoff from Royal Irrigation Department and Meteorological Department during flood period 2000-2012 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Nash Sutcliffe Efficiency (NSE) and 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. NSE and $R^2$ values for frist day of runoff forecasting is 0.76 and 0.776, respectively. On the second day, those values are 0.61 and 0.65, respectively. For the third day, the aforementioned valves are 0.51 and 0.52, respectively. The results confirmed that the ANNs model can be used when the range of collected dataset is short and insufficient. In conclusion, the ANNs model is suitable for applying during flood incident because it is easy to use and does not require numerous parameters for simulating.

• The Southeast Asian review
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• 제24권1호
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• pp.191-221
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• 2014

South Sumatra's capital, Palembang, has long maintained a river-oriented transportation system. With road transportation's increased importance for exploiting natural resources, however, hundreds of roads have been constructed since the Dutch colonial period. This article examines how the construction of roads and bridges affected people's lives and social networks in Palembang, and what social and political significance it has in the context of a region in the postcolonial Indonesia, with a focus on the huge river called the Musi River, which horizontally crosses the city. After independence, there has been strong aspiration to link these two parts by road, and in 1965 the Musi Bridge (then the Sukarno Bridge) over the river was eventually opened. The construction of the bridge apparently initiated socioeconomic transformations and development in the region, including Ulu (the southern river bank)'s rapid urbanization. However, the features of regional development actually were prerequisites for "national" development. The regional development was impossible without financial support from the central government, and the local or regional aspiration for development was often supported only when it fitted with national envision. The Musi Bridge was a model case that fitted with such national envision. While it was the symbol of regional development, it was also celebrated as an exemplary sign of "national" development, by both Sukarno's government and Suharto's New Order regime. By analyzing the discussions and discourses regarding the Musi project since early 1950s, in addition to its social and economic impact after the construction, this article explores the continuities and changes in the roles and significance of the (construction of the) Musi Bridge with the changing political backstops in both regimes. Together with it, this article also aims to reexamine the interplay between "the national" and "the regional" in the prevalent aspiration for the national and regional "development" throughout the 1950s and 1960s.

• Journal of Korean Society on Water Environment
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• 제39권1호
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Water Engineering Research
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• 제3권2호
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• pp.143-153
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• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• Journal of Korean Society on Water Environment
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• 제23권5호
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• pp.697-704
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• 2007

This study proposes an integrated technique of Genetic Algorishim (GA) and Geographic Information System (GIS) for designing the water quality monitoring networks. To develop solution scheme of the integrated system, fitness functions are defined by the linear combination of five criteria which stand for the operation objectives of water quality monitoring stations. The criteria include representativeness of a river system, compliance with water quality standards, supervision of water use, surveillance of pollution sources and examination of water quality changes. The fitness level is obtained through calculations of the fitness functions and input data from GIS. To find the most appropriate parameters for the problems, the sensitivity analysis is performed for four parameters such as number of generations, population sizes, probability of crossover, and probability of mutation. Using the parameters resulted from the sensitivity analysis, the developed system proposed 110 water quality monitoring stations in the Nakdong River. This study demonstrates that the integrated technique of GA and GIS can be utilized as a decision supporting tool in optimized design for a water quality monitoring network.

• Journal of Korea Water Resources Association
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• 제33권4호
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• pp.483-493
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• 2000

Two dimensional finite element model, RMA, is used to simulate flood inundation phenomena from main channel to floodplain. The marsh porosity method allows finite elements to simulate gradual transition between wet and dry states. The model is applied to prismatic trapezoidal channel to test the applicability of wetting and drying. The floodwave in a river which meanders through a floodplain is also analyzed. The short-circuiting effects, in which the flow leave the meandering main channel and takes a more direct route on the floodplain, are analyzed with various sinuosity factor and roughness coefficients. Finally, the model is applied to the midstream of the Keum River. Wet/dry calculation can simulate the various discharge condition with the same finite element networks.