• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.490-490
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• 2022

국내 대하천 및 중·소규모 하천의 홍수터 공간을 활용하여 체육시설 및 공원 등과 같은 친수 시설물을 조성하여 친수공간으로 활용하고 있으며, 시민들의 친수활동 빈도는 증가추세에 있다. 특히 하천 내에서 수상 레크레이션 활동 등과 같은 다양한 친수활동이 증가하고 있으며, 하천친수에 관한 정보 수요가 급증하고 있으나 체계적인 공급은 미흡한 수준이다. 따라서 본 연구에서는 친수공간 조성 및 유지관리에 대한 측면과 친수공간에서의 쾌적한 친수활동을 위한 정보제공 목적으로 하천 친수공간에서의 복합모니터링을 이용한 친수활동 종합지수를 산정 방법을 개발하고자 하였다. 센서 기반의 시계열 데이터 구축을 위해 하천 수질, 수리인자의 복합모니터링을 진행하였다. 수리인자(수위, 유속, 수면폭 등)와 수질인자(탁도, Chl-a, pH 등), 기상학적 인자(자외선 지수, 미세먼지 등) 등급에 따른 허용기준을 설정하여 각 등급 별로 수리인자의 값을 0~1 사이 값인 소속도로 변환하여 소속도의 합성 및 친수활동 등급을 결정하였다. 최종적으로 수리, 수질, 기상 인자별 소속도 함수 산정을 통한 퍼지합성 이론 기반의 친수활동 종합지수를 산정하였다. 그리고 친수활동 종합지수를 예보하기 위한 모델 적용을 위한 방향성을 정립하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.501-501
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• 2023

하천에서의 여가활동에 대한 수요가 증가함에 따라 각종 친수활동에 대한 안전도 평가가 사고예방을 위해 중요해지고 있다. 친수 활동의 안전은 수리 및 수질 인자에 크게 영향을 받지만 기존 친수지수는 수질 인자에만 집중되어 개발되어왔다. 하지만, 세일링, 패들링, 저동력보트 등 입수형 친수활동의 경우, 다양한 수리 현상에 큰 영향을 받기 때문에 유속, 흐름 방향, 수심 및 수면 폭 등의 수리인자를 친수지수에 반영할 필요가 있다. 또한, 친수활동에 위험이 되는 수리적 조건은 유량 조건과 하천의 평면적 공간에 따라 상이하게 발생하기에 이를 공간적으로 평가하는 것 역시 필요한 실정이다. 본 연구에서는 수리학적 요소를 기반으로 하천 친수 활동에 대한 안전도를 평가하기 위해 공간적으로 친수활동의 안정성을 평가할 수 있는 SRRI (Spatial River Recreation Index)를 제안하였다. SRRI의 개발을 위해 1단계에서는 다양한 유량 조건에서 EFDC 동수역학모형을 이용하여 수리 인자들의 공간적 분포를 재현한 후, 2단계에서는 퍼지합성법 (FSE)를 적용하여 수리인자의 모든 소속도와 가중치를 종합하여 하천 지점별 하천친수지수를 산정하였다. 개발한 SRRI를 낙동강-금호강 합류부에 적용한 결과, 유량 및 지형 조건에 따라 각 수리인자가 친수활동 안전성에 미치는 영향이 공간적으로 매우 상이하게 나타났다. 유향(흐름 방향)은 합류지점 부근에서 친수활동의 위험성을 크게 증가시키는 반면, 사행구간에서는 수심이 중요한 요인으로 나타났다. 고유량 조건에서는 유속이 세일링 및 패들링에서 가장 큰 영향을 미치는 요소로 작용하였다. 특히 세일링은 유량 변화에 민감하여 고유량시에는 주흐름부와 합류부 부근을 제외하고 일부 공간에서만 안전하게 이용이 가능한 것으로 나타났다. 반면 무동력 및 저동력보트는 유량 변화에 덜 민감하여 고유량 조건에서도 부분적으로 허용될 수 있었지만 사행구간의 고수심부에서는 위험 등급으로 권고되었다. 이러한 결과를 바탕으로 SRRI는 다양한 수리학적 조건을 기반으로 공간적 안전정보를 제공함으로써 많은 이용자들이 하천에서 보다 안전한 친수활동을 즐기는 데에 기여할 수 있을 것으로 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1395-1408
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• 2014

In this research, a River Recreation Index Model (RRIM) was developed to provide sufficient information on the water quality of rivers to the public in order to secure safety of publics. River Recreation Index (RRI) is an integrated water quality information for recreation activities in rivers and expressed as the point from 0 to 100. The proposed RRIM consisted of two sub models: Fecal Coliform Model (FCM) and Water Quality Index Model (WQIM). FCM predicted Fecal Coliform Grade (FCG) using a logistic regression and WQIM synthesized water quality parameters of, DO, pH, turbidity and chlorophyll a into Water Quality Index (WQI). FCG and WQI were integrated into RRI by the integrating algorithm. The proposed model was applied to upstream of Gangjeong Weir in Nakdong River, and compared with Real Time Water Quality Index (RTWQI) which is the existing water quality information system for recreation use. The results show that calculated RRI reflected change of integrated water quality parameters well. Especially chlorophyll a showed Pearson correlation coefficient -0.85 with RRI. Also, RRIM produced more conservative index than RTWQI because RRI was calculated considering uncertainty of water quality criteria. Further, RRI showed especially low values when fecal coliform was predicted as low grade.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.343-350
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• 2000

In this paper, we propose the Multi-FPNN(Fuzzy Polynomial Neural Networks) model based on FNN and PNN(Polyomial Neural Networks) for optimal system identifacation. Here FNN structure is designed using fuzzy input space divided by each separated input variable, and urilized both in order to get better output performace. Each node of PNN structure based on GMDH(Group Method of Data handing) method uses two types of high-order polynomials such as linearane and quadratic, and the input of that node uses three kinds of multi-variable inputs such as linear and quadratic, and the input of that node and Genetic Algorithms(GAs) to identify both the structure and the prepocessing of parameters of a Multi-FPNN model. Here, HCM clustering method, which is carried out for data preproessing of process system, is utilized to determine the structure method, which is carried out for data preprocessing of process system, is utilized to determance index with a weighting factor is used to according to the divisions of input-output space. A aggregate performance inddex with a wegihting factor is used to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of this aggregate abjective function which it is acailable and effective to design to design and optimal Multi-FPNN model. The study is illustrated with the aid of two representative numerical examples and the aggregate performance index related to the approximation and generalization abilities of the model is evaluated and discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2911-2913
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• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.