• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.220-220
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• 2015

Water, energy, and food security already became a risk that threatens people around the world. Increasing of resources demand, rapid urbanization, decreasing of natural resources and climate change are four major problems inducing resources' scarcity. Indeed, water, energy, and food are interconnected each other thus cannot be analyzed separately. That is, for simple example, energy needs water as source for hydropower plant, water needs energy for distribution, and food needs water and energy for production, which is defined as W-E-F nexus. Due to their complicated linkage, it needs a computer model to simulate and analyze the nexus. Development of a computer simulation model using system dynamics approach makes this linkage possible to be visualized and quantified. System dynamics can be defined as an approach to learn the feedback connections of all elements in a complex system, which mean, every element's interaction is simulated simultaneously. Present W-E-F nexus models do not calculate and simulate the element's interaction simultaneously. Existing models only calculate the amount of water and energy resources that needed to provide food, water, or energy without any interaction from the product to resources. The new proposed model tries to cope these lacks by adding the interactions, climate change effect, and government policy to optimize the best options to maintain the resources sustainability. On this first phase of development, the model is developed only to learn and analyze the interaction between elements based on scenario of fulfilling the increasing of resources demand, due to population growth. The model is developed using the Vensim, well-known system dynamics model software. The results are amount of total water, energy, and food demand and production for a certain time period and it is evaluated to determine the sustainability of resources.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.407-407
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• 2019

According to Asian Development Bank report Pakistan is among water scarce countries. Climate scenario on the basis IPCC fifth assessment report (AR5) revealed that annual mean temperature of Pakistan from year 2010-2019 was $17C^o$ which will rise up to $21C^o$ at the end of this century, similarly almost 10% decrease of annual rainfall is expected at the end of the century. It is a changing task in underdeveloped countries like Pakistan to meet the water demands of rapidly increasing population in a changing climate. While many studies have tackled scarcity and stream flow forecasting of the Upper Indus Basin (UIB) Pakistan, very few of them are related to socio-economic and climate change impact on sustainable water management of UIB. This study investigates the pattern of current and future surface water availability for various demand sites (e.g. domestic, agriculture and industrial) under different socio-economic and climate change scenarios in Upper Indus Basin (UIB) Pakistan for a period of 2010 to 2050. A state-of-the-art planning tool Water Evaluation and Planning (WEAP) is used to analyze the dynamics of current and future water demand. The stream flow data of five sub catchment (Astore, Gilgit, Hunza, Shigar and Shoyke) and entire UIB were calibrated and validated for the year of 2006 to 2011 using WEAP. The Nash Sutcliffe coefficient and coefficient of determination is achieved ranging from 0.63 to 0.92. The results indicate that unmet water demand is likely to increase severe threshold and the external driving forces e.g. socio-economic and climate change will create a gap between supply and demand of water.

• Journal of Korean Society of Water and Wastewater
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• v.23 no.6
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• pp.811-823
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• 2009

Seawater desalination process using a reverse osmosis (RO) membrane has been considered as one of the most promising technologies in solving the water scarcity problems in many arid regions around the world. To protect RO membrane in the process, a thorough understanding of the pretreatment process is particularly needed. Seawater organic matters (SWOMs) may form a gel layer on the membrane surface, which will increase a concentration polarization. As the SWOMs can be utilized as a substrate, membrane biofouling will be progressed on the RO membrane surface, resulting in the flux decline and increase of trans-membrane pressure drop and salt passage. In the middle of disinfection, an optimal chlorine dosage and neutralizer (sodium bisulfite, SBS) should be practiced to prevent oxidizing the surface of RO membranes. Additional fundamental research including novel non-susceptible biofouling membranes would be necessary to provide a guide line for the proper pretreatment process.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.37-47
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• 2017

In this study, we analyzed the variability of irrigation water amounts based on the combination of various crops and soil textures using the Irrigation Water Management Model (IWMM). IWMM evaluates the degree of agricultural drought using the Soil Moisture Deficit Index (SMDI). When crops are damaged by the water scarcity under the drought condition indicating that the SMDI values are in negative (SMDI<0), IWMM irrigates appropriate water amounts that can shift the negative SMDI values to "0" to crop fields. To test the IWMM model, we selected the Bandong-ri (BDR) and Jucheon (JC) sites in Gangwon-do and Jeollabuk-do provinces. We derived the soil hydraulic properties using the near-surface data assimilation scheme form the Time Domain Reflectrometry (TDR)-based soil moisture measurements. The daily root zone soil moisture dynamics (R: 0.792/0.588 and RMSE: 0.013/0.018 for BDR/JC) estimated by the derived soil parameters were matched well with the TDR-based measurements for validation. During the long-term (2001~2015) period, IWMM irrigated the minimum water amounts to crop fields, while there were no irrigation events during the rainy days. Also, Sandy Loam (SL) and Silt (Si) soils require more irrigation water amounts than others, while the irrigation water were higher in the order of radish, wheat, soybean, and potato, respectively. Thus, the IWMM model can provide efficient irrigation water amounts to crop fields and be useful for regions at where limited water resources are available.

• Membrane Journal
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• v.30 no.5
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• pp.293-306
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• 2020

Increasing harmful effects of climate change, such as its effect on water scarcity, has led to a focus on developing effective water purification methods to obtain pure water. Additionally, rising levels of water pollution is increasing levels of environmental degradation, calling for sources of water treatment to remove contaminants. To purify water, osmotic processes across a semipermeable membrane can take place, and recent studies are showing that incorporating nanoparticles, including carbon quantum dots (CQDs), graphene carbon dots (GQDs), and graphene oxide quantum dots (GOQDs) are making thin film composite (TFC) membranes more effective by increasing water flux while maintaining similar levels of salt rejection, increasing the hydrophilicity of the membrane surface, showing bactericidal properties, exhibiting antifouling properties to prevent accumulation of bacteria or other microorganisms from reducing the effectiveness of the membrane, and more. In the review, the synthesis process, applications, functionality, properties, and the role of several types of quantum dots are discussed in the composite membrane for water purification.

• Journal of Korean Society of Water and Wastewater
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• v.30 no.3
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• pp.285-291
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• 2016

There has been an accelerating increase in water reuse due to growing world population, rapid urbanization, and increasing scarcity of water resources. However, it is well recognized that water reuse practice is associated with many human health and ecological risks due to numerous chemicals and pathogenic microorganisms. Especially, the potential transmission of infectious disease by hundreds of pathogenic viruses in wastewater is one of the most serious human health risks associated with water reuse. In this study, we determined the response of different bacteriophages representing various bacteriophage groups to chlorination in real wastewater in order to identify a more reliable bacteriophage indicator system for chlorination in wastewater. Different bacteriophages were spiked into secondary effluents from wastewater plants from three different geographic areas, and then subjected to various doses of free chlorine and contact time at $5^{\circ}C$ in a bench-scale batch disinfection system. The inactivation of ${\phi}X174$ was relatively rapid and reached ~4 log10 with a CT value of 5 mg/L*min. On the other hand, the inactivation of bacteriophage PRD1 and MS2 were much slower than the one for ${\phi}X174$ and only ~1 log10 inactivation was achieved by a CT value of 10 mg/L*min. Overall, the results of this study suggest that bacteriophage both MS2 and PRD1 could be a reliable indicator for human pathogenic viruses for chlorination in wastewater treatment processes and water reuse practice.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.493-502
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• 2020

Traditionally, road projects are initiated based on an assessment of their economic benefit, after which the environmental, social and governance effects are addressed discretely for the project according to a set of predetermined alternatives. Sustainable road infrastructure planning is vital as issues like diminishing access to road construction supplies, water scarcity, Greenhouse Gas emissions, road-related fatalities and congestion pricing etc., have imposed severe economic, social, and environmental damages to the society. In the process of addressing these sustainability factors in the operational phase of the project, the dynamics of these factors are generally ignored. This paper argues that effective delivery of sustainable roads should consider such dynamics and highlights how different aspects of sustainability have the potential to affect project sustainability. The paper initially presents the different sustainability-assessment tools that have been developed to determine the sustainability performance of road projects and discuss the inability of these tools to model the interrelationships among sustainability-related factors. The paper then argues the need for a new assessment framework that facilitates modelling these dynamics at the macro-level (system level) and helping policymakers for sustainable infrastructure planning through evaluating regulatory policies.

• Journal of Korea Water Resources Association
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• v.42 no.8
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• pp.673-680
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• 2009

Alternatives to conventional water resources are being sought due to the scarcity and the poor quality of surface water. Riverbank filtration (RBF) is one of them and considered as a promising source of water supply in some cities. Changwon City has started RBF in 2001 and field data have been accumulated. This study is to develop a time-series model for groundwater level data collected from the pumping area of RBF. The site is Daesan-myeon, Changwon City, where groundwater level data have been measured for the last five years (Jan. 2003$\sim$Dec. 2007). Minute-based groundwater levels was averaged out to monthly data to see the long-term behavior. Time-series analysis was conducted according to the Box-Jenkins method. The resulted model turned out to be a seasonal ARIMA model, and its forecasting performance was satisfactory. We believe this study will provide a prototype for other riverbank filtration sites where the predictability of groundwater level is essential for the reliable supply of water.