• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.183-183
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• 2023

Smart water cities (SWC) are urban municipalities that utilizes modern innovations in managing and preserving the urban water cycle in the city; with the purpose of securing sustainability and improving the quality of life of the urban population. Understanding the different urban water characteristics and management strategies of cities situate a baseline in the development of evaluation scheme in determining whether the city is smart and sustainable. This research herein aims to develop measurements and evaluation for SWC Key Performance Indicators (KPIs), and set up a unified global standard and certification scheme. The assessment for SWC is performed in technical, as well as governance and prospective aspects. KPI measurements under Technical Pillar assess the cities' use of technologies in providing sufficient water supply, monitoring water quality, strengthening disaster resilience, minimizing hazard vulnerability, and maintaining and protecting the urban water ecosystem. Governance and Prospective Pillar on the other hand, evaluates the social, economic and administrative systems set in place to manage the water resources, delivering water services to different levels of society. The performance assessment is composed of a variety of procedures performed in a quantitative and qualitative manner, such as computations through established equations, interviews with authorities in charge, field survey inspections, etc. The developed SWC KPI measurements are used to evaluate the urban water management practices for Busan Eco Delta city, a Semulmeori waterfront area in Gangseo district, Busan. The evaluation and scoring process was presented and established, serving as the basis for the application of the smart water city certification all over the world. The established guideline will be used to analyze future cities, providing integrated and comprehensive information on the status of their urban water cycle, gathering new techniques and proposing solutions for smarter measures.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.110-122
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• 2023

Over the last few decades, various innovative technologies have emerged that have significantly contributed to making life easier for humans. Various information and communication technologies (ITCs) have emerged as a result of the global technological revolution, including big data, IoT, 4G and 5G networks, cloud computing, mobile computing, and artificial intelligence. These technologies have been adopted in urban planning and development, which gave rise to the concept of smart cities in the 1990s. A smart city is a type of city that uses ITCs to exchange and share information to enhance the quality of services for its citizens. With the global population increasing at unprecedented levels, cities are overwhelmed with a myriad of challenges, such as the energy crisis, environmental pollution, sanitation and sewage challenges, and water quality issues, and therefore, have become a convergence point of economic, social, and environmental risks. The concept of a smart city is a multidisciplinary, unified approach that has been adopted by governments and municipalities worldwide to overcome these challenges. Though challenging, this transformation is essential for cities with differing technological and social features, which all have the potential to determine the success or failure of the digital transformation of cities into smart cities. In recent years, researchers, businesses, and the government have all turned their attention to the emerging field of smart cities. Accordingly, this paper aims to represent a thorough understanding of the movement toward smart cities. The key themes identified are smart city definitions and concepts, smart city dimensions, and smart city architecture of different layers. Furthermore, this article discusses the challenges and some examples of smart cities.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1001-1008
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• 2022

As a part of the Smart Cities Mission, the Government of India in 2015 embarked upon the development of 100 existing cities as smart cities. In this study, the authors selected Ahmedabad city as the smart city development in India and presented project-level elements of the city based on the secondary data availability. At first, the authors focused on peer-reviewed articles, policy documents, and technical reports. Next, the authors collected the secondary data of project-level elements of the Ahmedabad city from the years 2015 to 2019. The findings show no significant improvement in the sewage system and waste collection as compared to the level of investment made in these sectors. The study showed that the water supply system outperformed revenue generation based on the government investment made in that sector. As a lesson learned, these findings indicate that significant improvement should be addressed in sewage management and waste collection. These study findings could help government officials, investors, developers, and city planners in making the appropriate decision before and during smart city execution. The lesson learned from this study could be used as a reference to improve revenue during the future smart city implication.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.505-505
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• 2022

This study shows the first output of the three-year project (2021-2023) to develop a Smart Water City (SWC) Global Standard and Certification Scheme ley by K-water, International Water Resources Association (IWRA) and Asia Water Council (AWC). There are three major parts in the first year. In Part 1, it investigates the essential features of cities today and details the water challenges currently faced and likely to be confronted in the future. It also investigates the functions that water fulfills in the urban environment, and how ICTs can contribute to improving those functions by each Urban Water Cycle. A definition of a Smart Water City is proposed following a discussion on the meaning of "smart development". This part of the report also presents different city cases from countries around the world to illustrate the urban water challenges and the technological and non-technological solutions that cities have put in place, including national and/or local policies and strategies. In Part 2, it defines what global standards indicators and certification schemes are and identifies their characteristics. Especially, it analyses in detail eight relevant standards and certification schemes measuring sustainable development and/or water resources management in urban settings. Standards elaborated by international organizations are distinguished from those developed by the private sector, non-governmental organizations, and by academia. Finally, this study suggests the right direction to develop SWC global standard frameworks and certification schemes. And then, it shows the main tasks for the Stage 2 (second year) project. Basically, the framework for a future SWC standard (consisting three main pillars: Technical, Governance and Prospective pillars) will be fully defined in Stage 2.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.113-120
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• 2023

Coastal and offshore structures such as ports and offshore wind farms will often need to be built on fine-grained sediments. Geotechnical properties associated with sediment compressibility are key parameters for marine construction designs especially on soft grounds, which involve clay-mineral dominated fines that can consolidate and settle significantly in response to engineered and environmental loads. We conduct liquid limit tests and 1D consolidation tests with fine-grained soils (silica silt, mica, kaolin and bentonite) and biogenic soils (diatom). The pore fluids for the liquid limit tests include deionized water and a series of brines with NaCl salt concentrations of 0.001 m, 0.01 m, 0.1 m, 0.6 m and 2.0 m, and the pore fluids for the consolidation tests deionized water, 0.01 m, 0.6 m, 2 m. The salt concentrations help the liquid limits of kaolin and bentonite decrease, but those of diatom slightly increase. The silica silt and mica show minimal changes in liquid limit due to salt concentrations. Accordingly, compression indices of soils follow the trend of the liquid limit as the liquid limit determined the initial void ratio of the consolidation test. Diatoms are more likely to be broken than clastic sediments during to loading, and diatom-rich sediment is therefore generally more compressible than clastic-rich sediment.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.135-135
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• 2022

In this study, we evaluated uncertainties in baseflow separation filter methods focusing on changes in recession constant (𝛼) values, which include Lynie & Holick (LH) algorithm, Chapman algorithm, Eckhardt filter, and EWMA filter. Here, we analyzed daily streamflow data at 14 stations in the Urmia Lake basin, Iran, from 2015 to 2019. The 𝛼 values were computed using three different approaches from calculating the slope of a recession curve by averaging the flow over all seasons, a correlation method, and a mean value of the ratio of Q_t+1 to Q_t. In addition to the 𝛼 values, the BFImax (maximum value of the baseflow index (BFI)) was determined for the Eckhardt filter through the backward filter method. As results, it indicates that the estimated baseflow is dependent upon the selection of filter methods, their parameters, and catchment characteristics at different stations. In particular, the EWMA filter showed the least changes in estimating the baseflow value by changing the 𝛼 value, and the Eckhardt filter and LH algorithm showed the highest sensitivity to this parameter at different stations.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.

• KCID journal
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• v.21 no.1
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• pp.127-140
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• 2014

Recently drastic climate changes(e.g., extreme floods and droughts) are often taking place around the world. Even an increase in uncertainty, population, and mega cities has caused drastic changes in water recycle process. As in other countries, Korea has faced some issues relevant to water security. In response to these changes, Smart Water Grid(SWG) system combining the current water resources management with ICT (Information and Communications Technology) is considered as a new paradigm for the Korean water resources management. This study aims to explore and identify influential factors contributing to the SWG system's application to analyze the importance and role of those factors, and then to offer a policy suggestion for the successful application of the SWG system along with legislative improvements in Korea. In this study, we looked at different barriers related to the SWG application and also the complicated Korean water laws, enacted by different ministries and in order to efficiently apply the SWG system to the current Korean water resources management structures. This study employed qualitative research methods to analyze and identify the priorities of the tasks to be implemented by analyzing conditions for the SWG application, especially related to multi water sources and micro water grid, because legal and institutional measures can be more important to manage conflicts between different stakeholders once the SWG enters a phase of standardization and commercialization from its development stage.