• Journal of Korean Society of Environmental Engineers
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• v.32 no.10
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• pp.916-927
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• 2010

In general water treatment process, the disinfection process by chlorine is used to prevent water borne disease and microbial regrowth in water distribution system. Because chlorines were reacted with organic matter, carcinogens such as disinfection by-products (DBPs) were produced in drinking water. Therefore, a suitable injection of chlorine is need to decrease DBPs. Rechlorination in water pipelines or reservoirs are recently increased to secure the residual chlorine in the end of water pipelines. EPANET 2.0 developed by the U.S. Environmental Protection Agency (EPA) is used to compute the optimal chlorine injection in water treatment plant and to predict the dosage of rechlorination into water distribution system. The bulk decay constant ($k_{bulk}$) was drawn by bottle test and the wall decay constant ($k_{wall}$) was derived from using systermatic analysis method for water quality modeling in target region. In order to predict water quality based on hydraulic analysis model, residual chlorine concentration was forecasted in water distribution system. The formation of DBPs such as trihalomethanes (THMs) was verified with chlorine dosage in lab-scale test. The bulk decay constant ($k_{bulk}$) was rapidly decreased with increasing temperature in the early time. In the case of 25 degrees celsius, the bulk decay constant ($k_{bulk}$) decreased over half after 25 hours later. In this study, there were able to calculate about optimal rechlorine dosage and select on profitable sites in the network map.

• Economic and Environmental Geology
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• v.3 no.1
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• pp.25-34
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• 1970

Very few articles are available on geologic structure and genesis of Sangdong scheelite-deposits in spite of the fact that the mine is one of the leading tungsten producer in the world. Sangdong scheelite deposits, embedded in Myobong slate of Cambrian age at the southem limb of the Hambaek syncline which strikes $N70{\sim}80^{\circ}W$ and dips $15{\sim}30^{\circ}$ northeast, comprise six parallel veins in coincide with the bedding plane of Myobong formation, namely four footwall veins, a main vein, and a hangingwall vein. Four footwall veins are discontinuous and diminish both directions in short distance and were worked at near surface in old time. Hangingwall vein is emplaced in brecciated zone in contact plane of Myobong slate and overlying Pungchon limestone bed of Cambrian age and has not been worked until recent. The main vein, presently working, continues more than 1,500 m in both strike and dip sides and has a thickness varying 3.5 to 5 m. Characteristic is the distinct zonal arrangement of the main vein along strike side which gives a clue to the genesis of the deposits. The zones symmetrically arranged in both sides from center are, in order of center to both margins, muscovite-biotite-quartz zone, biotite-hornblende-quartz zone and garnet-diopside zone. The zones grade into each other with no boundary, and minable part of the vein streches in the former two zones extending roughly 1,000 m in strike side and over 1,100 m in dip side to which mining is underway at present. The quartz in both muscovite-biotite-quartz and biotite-hornblende-quartz zones is not network type of later intrusion, but the primary constituent of the special type of rock that forms the main vein. The minable zone has been enriched several times by numerous quartz veins along post-mineral fractures in the vein which carry scheelite, molybdenite, bismuthinite, fluorite and other sulfide minerals. These quartz veins varying from few centimeter to few tens of centimeter in width are roughly parallel to the main vein although few of them are diagonal, and distributed in rich zones not beyond the vein into both walls and garnet-diopside zone. Ore grade ranges from 1.5~2.5% $WO_3$ in center zone to less than 0.5% in garnet-diopside zone at margin, biotite-hornblende-quartz zone being inbetween in garde. The grade is, in general, proportional to the content of primary quartz. Judging from regional structure in mid-central parts of South Korea, Hambaek syncline was formed by the disturbance at the end of Triassic period with which bedding thrust and accompanied feather cracks in footwall side were created in Myobong slate and brecciated zone in contact plane between Myobong slate and Pungchon limestone. These fractures acted as a pathway of hot solution from interior which was in turn differentiated in situ to form deposit of the main vein with zonal arrangement. The footwall veins were developed along feather cracks accompanied with the main thrust by intrusion of biotite-hornblende-quartz vein and the hangingwall vein in shear zone along contact plane by replacement. The main vein thus formed was enriched at later stage by hydrothermal solutions now represented by quartz veins. The main mineralization and subsequent hydrothermal enrichments had probably taken place in post-Triassic to pre-Cretaceous periods. The veins were slightly displaced by post-mineral faults which cross diagonally the vein. This hypothesis differs from those done by previous workers who postulated that the deposits were formed by pyrometasomatic to contact replacement of the intercalated thin limestone bed in Myobong slate at the end of Cretaceous period.

• Korean Journal of Environmental Biology
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• v.22 no.3
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• pp.351-368
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• 2004

Following the previous experiments, a starvation experiment was conducted to determine the influence of feeding and starvation on the histological and biochemical changes, the morphormetric changes in the sectioned body and the morphometric changes in Rhynchocypris oxycephalus (Sauvage and Dabry). The influence of starvation on nutritional conditions of the histological changes of hepatocyte and intestinal epithelium as hepatosmatic index (HSI), protein, RNA and DNA concentrations of liver in R. oxycephalus was tested. Although the starved group showed higher concentrations of protein, DNA and RNA than the fed group, food deprivation resulted in a decrease in the HSI, hepatocyte nucleus size and nuclear height of the intestinal epithelium. The RNA - DNA ratio appears to be a useful index of nutritional status in R. oxycephalus and may be useful for determining if R. oxycephalus is in a period of rapid or slow growth at the time of sampling. Additionally, the data have been interpreted in detail and some biologically important relationships discussed. The effects of starvation on the morphometrical changes in sectioned body traits, condition factor, viscera index and dressing percentage were determined for evaluating nutritional conditions of R. oxycephalus. Starvation for nine weeks resulted in a decrease in most sectioned traits as well as in condition factor and viscera index (P<0.05). These findings suggest that nutritional parameters used in this study appear to be a useful index for nutritional status in this species. The data has been interpreted in detail and some important body sectioned values of interest to commercial growers discussed. A 75-day study was conducted to determine the effect of starvation on classical and truss parameters in R. oxycephalus. Truss dimensions of almost the entire head and trunk region as well as the abdomen were increased significantly through feeding or starvation (P<0.05). Truss dimensions of the caudal region generally decreased through feeding or starvation, particularly those dimensions at the hind part of the trunk. There were some significant decreases in classical dimensions of the head region during feeding, in relation to body depth characteristics in the trunk and caudal region during starvation, whereas there was only one decreasing classical dimension in the caudal region during feeding. The results of this study indicate that application of the truss network as a character set enforces classical coverage across the body form, discrimination among experimental groups thus being enhanced. Considering that the dimension of the lower part of the head and some truss and classical dimensions were least affected by feeding and starvation, these dimensions may then be useful as a taxonomical indicator to discriminate the species of Rhynchocypris sp. The value of trunk region dimensions with a large component of body depth in R. oxycephalus is most likely to be compromised by variability related to differences in feeding regimes of fish in different habitats.

• Journal of Broadcast Engineering
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• v.8 no.4
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• pp.325-338
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• 2003

Video adaptation in response to dynamic resource conditions and user preferences is required as a key technology to enable universal multimedia access (UMA) through heterogeneous networks by a multitude of devices In a seamless way. Although many adaptation techniques exist, selections of appropriate adaptations among multiple choices that would satisfy given constraints are often ad hoc. To provide a systematic solution, we present a general conceptual framework to model video entity, adaptation, resource, utility, and relations among them. It allows for formulation of various adaptation problems as resource-constrained utility maximization. We apply the framework to a practical case of dynamic bit rate adaptation of MPEG-4 video streams by employing combination of frame dropping and DCT coefficient dropping. Furthermore, we present a descriptor, which has been accepted as a part of MPEG-21 Digital Item Adaptation (DIA), for supporting terminal and network quality of service (QoS) in an interoperable manner. Experiments are presented to demonstrate the feasibility of the presented framework using the descriptor.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.67-88
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• 2019

Webtoon is a Korean-style digital comics platform that distributes comics content produced using the characteristic elements of the Internet in a form that can be consumed online. With the recent rapid growth of the webtoon industry and the exponential increase in the supply of webtoon content, the need for effective webtoon content recommendation measures is growing. Webtoons are digital content products that combine pictorial, literary and digital elements. Therefore, webtoons stimulate consumer sentiment by making readers have fun and engaging and empathizing with the situations in which webtoons are produced. In this context, it can be expected that the sentiment that webtoons evoke to consumers will serve as an important criterion for consumers' choice of webtoons. However, there is a lack of research to improve webtoons' recommendation performance by utilizing consumer sentiment. This study is aimed at developing consumer sentiment pattern maps that can support effective recommendations of webtoon content, focusing on consumer sentiments that have not been fully discussed previously. Metadata and consumer sentiments data were collected for 200 works serviced on the Korean webtoon platform 'Naver Webtoon' to conduct this study. 488 sentiment terms were collected for 127 works, excluding those that did not meet the purpose of the analysis. Next, similar or duplicate terms were combined or abstracted in accordance with the bottom-up approach. As a result, we have built webtoons specialized sentiment-index, which are reduced to a total of 63 emotive adjectives. By performing exploratory factor analysis on the constructed sentiment-index, we have derived three important dimensions for classifying webtoon types. The exploratory factor analysis was performed through the Principal Component Analysis (PCA) using varimax factor rotation. The three dimensions were named 'Immersion', 'Touch' and 'Irritant' respectively. Based on this, K-Means clustering was performed and the entire webtoons were classified into four types. Each type was named 'Snack', 'Drama', 'Irritant', and 'Romance'. For each type of webtoon, we wrote webtoon-sentiment 2-Mode network graphs and looked at the characteristics of the sentiment pattern appearing for each type. In addition, through profiling analysis, we were able to derive meaningful strategic implications for each type of webtoon. First, The 'Snack' cluster is a collection of webtoons that are fast-paced and highly entertaining. Many consumers are interested in these webtoons, but they don't rate them well. Also, consumers mostly use simple expressions of sentiment when talking about these webtoons. Webtoons belonging to 'Snack' are expected to appeal to modern people who want to consume content easily and quickly during short travel time, such as commuting time. Secondly, webtoons belonging to 'Drama' are expected to evoke realistic and everyday sentiments rather than exaggerated and light comic ones. When consumers talk about webtoons belonging to a 'Drama' cluster in online, they are found to express a variety of sentiments. It is appropriate to establish an OSMU(One source multi-use) strategy to extend these webtoons to other content such as movies and TV series. Third, the sentiment pattern map of 'Irritant' shows the sentiments that discourage customer interest by stimulating discomfort. Webtoons that evoke these sentiments are hard to get public attention. Artists should pay attention to these sentiments that cause inconvenience to consumers in creating webtoons. Finally, Webtoons belonging to 'Romance' do not evoke a variety of consumer sentiments, but they are interpreted as touching consumers. They are expected to be consumed as 'healing content' targeted at consumers with high levels of stress or mental fatigue in their lives. The results of this study are meaningful in that it identifies the applicability of consumer sentiment in the areas of recommendation and classification of webtoons, and provides guidelines to help members of webtoons' ecosystem better understand consumers and formulate strategies.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.2
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• pp.1-12
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• 2015

This study explores urban gardening and garden culture in residential area as an everydayness that has been overlooked during the modern period urbanization and investigates the meaning and value of urban gardening from the perspective of urban formations and growth in spontaneous urban residential area, Haebangchon. The result identified that urban gardening as a meaning of contemporary culture is a new clue to improving the urban physical environment and changing the lives and community network of residents. Haebangchon is one of the few remaining spontaneous habitations in Seoul, and was created as a temporary unlicensed shantytown in 1940s. It became the representative habitation for common people in downtown Seoul through the revitalization of the 60s and the local reform through self-sustaining redevelopment projects during the 70s through the 90s. This area still contains the image of times during the 50s to the 60s, the 70s to the 80s and present, with the percentage of long-term stay residents high. Within this context, the site is divided into third quarters, and the research undertaken by observation and investigation to determine characteristics of urban gardening as an everydayness. It can be said that urban gardening and garden culture in Haebangchon is a unique location culture that has accumulated in the crevices of the physical condition and culture of life. These places are an expression of resident's desires that seeking out nature and gardening as revealed in densely-populated areas and the grounds of practical acting and participating in care and cultivation. It forms a unique, indigenous local landscape as an accumulation of everyday life of residents. Urban gardens in detached home has retained the original function of the dwelling and the garden, or 'madang', and takes on the characteristic of public space through the sharing of a public nature as well as semi-private spatial characteristic. Also, urban gardens including small kitchen garden and flowerpots that appear in the narrow streets provide pleasure as a part of nature that blossoms in narrow alley and functions as a public garden for exchanging with neighbors by sharing produce. This paper provides the concept of redefining the relationship between the private-public area that occurs between outside spaces that are cut off in a modern city.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6099-6111
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• 2015

This study was a systematic review and meta-analysis comparing the effects of aquatic exercise and land-based exercise in the treatment of knee osteoarthritis. 7 studies (n=449) met selection and exclusion criteria out of 287 potential studies obtained from the literature search via Ovid-Medline, Cochrane Library CENTRAL, CINAHL, RISS and KISS. The overall risk of bias of selected studies using SIGN (Scottish Intercollegiate Guidelines Network) checklist for randomized controlled trials (RCT) was regarded as low. As a result of meta analysis, Standardized Mean Difference (SMD) for pain was -0.26(95% CI -0.49, -0.03, p=0.03, $I^2=14%$), which implies that aquatic exercise groups had significant less pain than land-based exercise groups. On the other hand, there was no significant difference between aquatic exercise groups and land based exercise groups for flexion Range of Motion (ROM) (-0.12, 95% CI -0.51, 0.27, p=0.53, $I^2=0%$), extension ROM (-0.04, 95% CI -0.55, 0.48, p=0.89, $I^2=43%$), physical function (-0.12, 95% CI -0.44, 0.19, p=0.44, $I^2=0%$), Quality of Life (QOL) (-0.15, 95% CI -0.54, 0.24, p=0.46, $I^2=0%$). This study has some limitations due to few RCTs comparing aquatic exercise groups and land-based exercise groups in the treatment of knee osteoarthritis. Therefore, further RCTs should be conducted along with long-term outcomes.