• 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.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Journal of Biosystems Engineering
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• v.42 no.4
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• pp.350-357
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• 2017

Purpose: In-situ monitoring of water quality is fundamental to most environmental applications. The high cost and long delays of conventional laboratory methods used to determine water quality, including on-site sampling and chemical analysis, have limited their use in efficiently managing water sources while preventing environmental pollution. The objective of this study was to develop an on-site water monitoring system consisting mainly of an Arduino board and a sensor array of multiple ion selective electrodes (ISEs) to measure the concentration of $NO_3$ ions. Methods: The developed system includes a combination of three ISEs, double-junction reference electrode, solution container, sampling system consisting of three pumps and solenoid valves, signal processing circuit, and an Arduino board for data acquisition and system control. Prior to each sample measurement, a two-point normalization method was applied for a sensitivity adjustment followed by an offset adjustment to minimize the potential drift that could occur during continuous measurement and standardize the response of multiple electrodes. To investigate its utility in on-site nitrate monitoring, the prototype was tested in a facility where drinking water was collected from a water supply source. Results: Differences in the electric potentials of the $NO_3$ ISEs between 10 and $100mg{\cdot}L^{-1}$ $NO_3$ concentration levels were nearly constant with negative sensitivities of 58 to 62 mV during the period of sample measurement, which is representative of a stable electrode response. The $NO_3$ concentrations determined by the ISEs were almost comparable to those obtained with standard instruments within 15% relative errors. Conclusions: The use of the developed on-site nitrate monitoring system based on automatic sampling and two-point normalization was feasible for detecting abrupt changes in nitrate concentration at various water supply sites, showing a maximum difference of $4.2mg{\cdot}L^{-1}$ from an actual concentration of $14mg{\cdot}L^{-1}$.

• Environmental Analysis Health and Toxicology
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• v.16 no.4
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• pp.161-170
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• 2001

In aqueous ecosystems, the level of toxicity is highly responsive dependant to multiple variables, including rainfall, sunlight, pH, adhesion, etc. Because Korea has particularly distinct wet and dry seasons, the toxicity of pollutants in rivers or streams is dependant on the sampling season and time. In order to examine the effects of rainfall on toxicity, sediment samples were collected from five sites along the Keumho river. It was found that Microtox toxicity levels were generally higher during the dry season than the wet season. It indicated that river pollutants are carried off more quickly by the water during the wet season. As a result, it was recommended that the point sources of pollutants of the Keumho river would be placed between KH3 (Paldalgyo) and KH4(Keumhogyo), KH4(Keumhogyo) and KH5(Dasa).

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.08a
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• pp.75-87
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• 2005

Remote sensing(RS)- and geographic information system(GIS)-based information management to measure and assess agri-environment schemes, and to quantify and map environment indicators for nature and land use, climate change, air, water and energy balance, waste and material flow is in high demand because it is very helpful in assisting decision making activities of farmers, government, researchers, and consumers. The versatility and ability of RS and GIS containing huge soil database to assess agricultural environment spatially and temporally at various spatial scales were investigated. Spectral and microwave observations were carried out to characterize crop variables and soil properties. Multiple sources RS data from ground sensors, airborne sensors, and also satellite sensors were collected and analyzed to extract features and land cover/use for soils, crops, and vegetation for support precision agriculture, soil/land suitability, soil property estimation, crop growth estimation, runoff potential estimation, irrigated and the estimation of flooded areas in paddy rice fields. RS and GIS play essential roles in a management and monitoring information system. Biosphere-atmosphere interection should also be further studied to improve synergistic modeling for environment and sustainability in agri-environment schemes.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.787-791
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• 2017

Americium-beryllium (Am-Be; n, ${\gamma}$) is a neutron emitting source used in various research fields such as chemistry, physics, geology, archaeology, medicine, and environmental monitoring, as well as in the forensic sciences. It is a mobile source of neutron activity (20 Ci), yielding a small thermal neutron flux that is water moderated. The aim of this study is to develop a model to increase the neutron thermal flux of a source such as Am-Be. This study achieved multiple advantageous results: primarily, it will help us perform neutron activation analysis. Next, it will give us the opportunity to produce radio-elements with short half-lives. Am-Be single and multisource (5 sources) experiments were performed within an irradiation facility with a paraffin moderator. The resulting models mainly increase the thermal neutron flux compared to the traditional method with water moderator.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.591-595
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• 2010

Conventional methods of model evaluation usually rely only on model performance based on a comparison of simulated variables to corresponding observations. However, this type of model evaluation has been criticized because of its insufficient consideration of the various uncertainty sources involved in modeling processes. This study aims to propose an extended model evaluation method using multiple assesment indices (MAIs) that consider not only the model performance but also the model structure and parameter uncertainties in rainfall-runoff modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250m, 500m, and 1km digital elevation models) were developed and assessed by three MAIs for model performance, model structural stability, and parameter identifiability. All the models provided acceptable performance in terms of a global response, but the simpler SFM and KWMSS1 could not accurately represent the local behaviors of hydrographs. In addition, SFM and KWMSS1 were structurally unstable; their performance was sensitive to the applied objective functions. On the other hand, the most sophisticated model, KWMSS2, performed well, satisfying both global and local behaviors. KMSS2 also showed good structural stability, reproducing hydrographs regardless of the applied objective functions; however, superior parameter identifiability was not guaranteed. Numerous parameter sets could lead to indistinguishable hydrographs. This result supports that while making a model complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty. The proposed model evaluation process can provide an effective guideline for identifying a reliable hydrologic model.

• Journal of Wetlands Research
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• v.21 no.2
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• pp.102-113
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• 2019

Slack-tide sampling was carried out at 6 stations at high and low tide for a tidal cycle during spring tide of the early summer (June) and summer (July, August) of 2016 to determine the difference of water quality according to tide in Masan Bay, Korea. The mixing regime of all the water quality components investigated was well explained through the correlation with SAL. In the early summer and summer, TURB, DSi and NNN which mainly flow into the bay from the streams and SS, COD, AMN and $H_2S$ which mainly indicate the internal sink and source materials have a property of conservative mixing and non-conservative mixing, respectively. The conservative mixing showed a good linear relationship of the water quality between high and low tide, and the non-conservative mixing showed a variation of different pattern each other. Factor analysis performed on the concentration difference data sets between high and low tide helped in identifying the principal latent variables for them. In early summer, multiple effects (tidal action, natural influx and internal sinks and sources etc.) acted in combination for the differences to be distributed evenly in four factors (VF1~4), since there were few allochthonous inputs as a low-water season. On the contrary, in summer, the parameters showing large concentration difference at ST-1 affected by stream water were concentrated in one factor (VF1) and clearly distinguished from the parameters affected by the internal sinks and sources. In fact, there is no estuary (bay) that always maintains steady state flow conditions. The mixing regime of an estuary might be changed at any time due to the change of flushing time, and furthermore the change of end-member conditions due to the internal sinks and sources makes the occurrence of concentration difference inevitable. Therefore, when investigating the water quality of the estuary, it is necessary to take a sampling method considering the tide to obtain average water quality data.

• Journal of Information Science Theory and Practice
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• v.7 no.4
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• pp.56-64
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• 2019

Planting intercropping in rubber plantations is another alternative for generating more income for farmers. However, farmers still lack the knowledge of choosing plants. In addition, information for decision making comes from many sources and is knowledge accumulated by the expert. Therefore, this research aims to create a decision support system for growing rubber trees for individual farmers. It aims to get the highest income and the lowest cost by using semantic web technology so that farmers can access knowledge at all times and reduce the risk of growing crops, and also support the decision supporting system (DSS) to be more intelligent. The integrated intercropping ontology and rule are a part of the decision-making process for selecting plants that is suitable for individual rubber plots. A list of suitable plants is important for decision variables in the allocation of planting areas for each type of plant for multiple purposes. This article presents designing and developing the intercropping ontology for DSS which defines a class based on the principle of intercropping in rubber plantations. It is grouped according to the characteristics and condition of the area of the farmer as a concept of the rubber plantation. It consists of the age of rubber tree, spacing between rows of rubber trees, and water sources for use in agriculture and soil group, including slope, drainage, depth of soil, etc. The use of ontology for recommended plants suitable for individual farmers makes a contribution to the knowledge management field. Besides being useful in DSS by offering options with accuracy, it also reduces the complexity of the problem by reducing decision variables and condition variables in the multi-objective optimization model of DSS.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.2
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• pp.235-248
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• 2014

The widespread occurrence of dissolved endocrine disrupting compounds(EDCs) and pharmaceutical active compounds(PhACs) in water sources is of concern due to their adverse effects. To remove these chemicals, adsorption of EDCs/PhACs on granular activated carbon(GAC) was investigated, and bisphenol A, carbamazepine, diclofenac, ibuprofen, and sulfamethoxazole were selected as commonly occurring EDCs/PhACs in the aquatic environment. Various adsorption isotherms were applied to evaluate compatability with each adsorption in the condition of single-solute. Removal difference between individual and competitive adsorption were investigated from the physicochemical properties of each adsorbate. Hydrophobicity interaction was the main adsorption mechanism in the single-solute adsorption with order of maximum adsorption capacity as bisphenol A > carbamazepine > sulfamethoxazole > diclofenac > ibuprofen, while both hydrophobicity and molecular size play significant roles in competitive adsorption. Adsorption kinetic was also controled by hydrophobicity of each adsorbate resulting in higher hydrophobicity allowed faster adsorption on available adsorption site on GAC. EDCs/PhACs adsorption on GAC was determined as an endothermic reaction resulting in better adsorption at higher temperature ($40^{\circ}C$) than lower temperature ($10^{\circ}C$).