A fuzzy dynamic flood routing model for natural channels

A fuzzy dynamic flood routing model (FDFRM) for natural channels is presented, wherein the flood wave can be approximated to a monoclinal wave. This study is based on modification of an earlier published work by the same authors, where the nature of the wave was of gravity type. Momentum equation of the dynamic wave model is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. Hence, the FDFRM gets rid of the assumptions associated with the momentum equation. Also, it overcomes the necessity of calculating friction slope (S_f) in flood routing and hence the associated uncertainties are eliminated. The fuzzy rule based model is developed on an equation for wave velocity, which is obtained in terms of discontinuities in the gradient of flow parameters. The channel reach is divided into a number of approximately uniform sub‐reaches. Training set required for development of the fuzzy rule based model for each sub‐reach is obtained from discharge‐area relationship at its mean section. For highly heterogeneous sub‐reaches, optimized fuzzy rule based models are obtained by means of a neuro‐fuzzy algorithm. For demonstration, the FDFRM is applied to flood routing problems in a fictitious channel with single uniform reach, in a fictitious channel with two uniform sub‐reaches and also in a natural channel with a number of approximately uniform sub‐reaches. It is observed that in cases of the fictitious channels, the FDFRM outputs match well with those of an implicit numerical model (INM), which solves the dynamic wave equations using an implicit numerical scheme. For the natural channel, the FDFRM outputs are comparable to those of the HEC‐RAS model. Copyright © 2009 John Wiley & Sons, Ltd.     

A fuzzy dynamic wave routing model

In this paper a fuzzy dynamic wave routing model (FDWRM) for unsteady flow simulation in open channels is presented. The continuity equation of the dynamic wave routing model is preserved in its original form while the momentum equation is replaced by a fuzzy rule based model which is developed on the principle that during unsteady flow the disturbances in the form of discontinuities in the gradient of the physical parameters will propagate along the characteristics with a velocity equal to that of velocity of the shallow water wave. The model gets rid off the assumptions associated with the momentum equation by replacing it with the fuzzy rule based model. It overcomes the necessity of calculating friction slope (S_f) in flow routing and hence the associated uncertainties are eliminated. The robustness of the fuzzy rule based model enables the FDWRM to march the solution even in regions where the aforementioned assumptions are violated. Also the model can be used for flow routing in curved channels. When the model is applied to hypothetical flood routing problems in a river it is observed that the results are comparable to those of an implicit numerical model (INM) which solves the dynamic wave equations using an implicit numerical scheme. The model is also applied to a real case of flow routing in a field canal. The results match well with the measured data and the model performs better than the INM. Copyright © 2007 John Wiley & Sons, Ltd.     

Sliding mode fuzzy control: Theory and verification on a benchmark structure

A sliding mode fuzzy control (SMFC) algorithm is presented for vibration reduction of large structures. The rule base of the fuzzy inference engine is constructed based on the sliding mode control, which is one of the non‐linear control algorithms. In general, fuzziness of the controller makes the control system robust against the uncertainties in the system parameters and the input excitation, and the non‐linearity of the control rule makes the controller more effective than linear controllers. For verification of the present algorithm, a numerical study is carried out on the benchmark problem initiated by the ASCE Committee on Structural Control. To achieve a high level of realism, various aspects are considered such as actuator–structure interaction, sensor noise, actuator time delay, precision of the A/D and D/A converters, magnitude of control force, and order of control model. Performance of the SMFC is examined in comparison with those of other control algorithms such as H_{mixed 2/∞}, optimal polynomial control, neural networks control, and SMC, which were reported by other researchers. The results indicate that the present SMFC is efficient and attractive, since the vibration responses of the structure can be reduced very effectively and the design procedure is simple and convenient. Copyright © 2000 John Wiley & Sons, Ltd.     

结构振动的模糊建模与模糊控制规则提取

模糊振动控制中存在的模糊控制规则的建立大都依赖于主观经验的现状。对此本文提出了一种通过对结构振动模糊建模来产生控制规则的方法。首先，通过对系统运动状态变量的模糊化，建立结构振动的模糊关系模型；其次通过对结构振动的模糊关系模型的分析，提取出模糊控制规则；最后，通过一个单自由度体系的数值仿真方法进行了验证。     

Semiactive fuzzy control of the seismic response of building frames with MR dampers

A fuzzy‐logic control algorithm, based on the fuzzification of the MR damper characteristics, is presented for the semiactive control of building frames under seismic excitation. The MR damper characteristics are represented by force–velocity and force–displacement curves obtained from the sinusoidal actuation test. The method does not require any analytical model of MR damper characteristics, such as the Bouc‐Wen model, to be incorporated into the control algorithm. The control algorithm has a feedback structure and is implemented by using the fuzzy‐logic and Simulink toolboxes of MATLAB. The performance of the algorithm is studied by using it to control the responses of two example buildings taken from the literature—a three‐storey building frame, in which controlled responses are obtained by clipped‐optimal control and a ten‐storey building frame. The results indicate that the proposed scheme provides nearly the same percentage reduction of responses as that obtained by the clipped‐optimal control with much less control force and much less command voltage. Position of the damper is found to significantly affect the controlled responses of the structure. It is observed that any increase in the damper capacity beyond a saturation level does not improve the performance of the controller. Copyright © 2011 John Wiley & Sons, Ltd.     

Flow Discharge Modeling in Open Canals Using a New Fuzzy Modeling Technique (SMRGT)

The main goal of this study was to research the answer to two important questions in flow modeling; i) how to optimally design the cross‐section of an open channel for a given flow, and ii) in the case of selecting the fuzzy method for modeling, how to construct the membership functions (MFs) and fuzzy rules (FRs) such that the system yields the best results. The first question is answered in order to minimize difficulties in excavation and related costs by using the appropriate flow velocity. To provide the best answer researchers use several methods. The second question is answered in order to minimize model error. For this aim, there are many algorithms proposed by researchers in the literature. In this paper, the fuzzy logic method was used for open canal flow modeling. Furthermore, a simple membership function and fuzzy rule generation technique (SMRGT) is introduced, and used for fuzzy modeling. Two fuzzy models, each for different cross‐sectional shape, are presented in this study as an application of SMRGT. The comparison depends on various statistics, mean absolute relative error, and contour maps showed that the fuzzy models were successful in open channel flow modeling and SMRGT is useful for MF (membership function) and FR (fuzzy rule) generation.     

Developing a hybrid multi‐model for peak flood forecasting

The present study aims to develop a hybrid multi‐model using the soft computing approach. The model is a combination of a fuzzy logic, artificial neural network (ANN) and genetic algorithm (GA). While neural networks are low‐level computational structures that perform well dealing with raw data, fuzzy logic deal with reasoning on a higher level by using linguistic information acquired from domain experts. However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment. Moreover, experts occasionally make mistakes and thus some rules used in a system may be false. A network type structure of the present hybrid model is a multi‐layer feed‐forward network, the main part is a fuzzy system based on the first‐order Sugeno fuzzy model with a fuzzification and a defuzzification processes. The consequent parameters are determined by least square method. The back‐propagation is applied to adjust weights of network. Then, the antecedent parameters of the membership function are updated accordingly by the gradient descent method. The GA was applied to select the fuzzy rule. The hybrid multi‐model was used to forecast the flood level at Chiang Mai (under the big flood 2005) and the Koriyama flood (2003) in Japan. The forecasting results are evaluated using standard global goodness of fit statistic, efficient index (EI), the root mean square error (RMSE) and the peak flood error. Moreover, the results are compared to the results of a neuro‐genetic model (NGO) and ANFIS model using the same input and output variables. It was found that the hybrid multi‐model can be used successfully with an efficiency index (EI) more than 0·95 (for Chiang Mai flood up to 12 h ahead forecasting) and more than 0·90 (for Koriyama flood up to 8 h ahead forecasting). In general, all of three models can predict the water level with satisfactory results. However, the hybrid model gave the best flood peak estimation among the three models. Therefore, the use of fuzzy rule base, which is selected by GA in the hybrid multi‐model helps to improve the accuracy of flood peak. Copyright © 2009 John Wiley & Sons, Ltd.     

Rule reduction in fuzzy logic for better interpretability in reservoir operation

Decision‐making in reservoir operation has become easy and understandable with the use of fuzzy logic models, which represent the knowledge in terms of interpretable linguistic rules. However, the improvement in interpretability with increase in number of fuzzy sets (‘low’, ‘high’, etc) comes with the disadvantage of increase in number of rules that are difficult to comprehend by decision makers. In this study, a clustering‐based novel approach is suggested to provide the operators with a limited number of most meaningful operating rules. A single triangular fuzzy set is adopted for different variables in each cluster, which are fine‐tuned with genetic algorithm (GA) to meet the desired objective. The results are compared with the multi fuzzy set fuzzy logic model through a case study in the Pilavakkal reservoir system in Tamilnadu State, India. The results obtained are highly encouraging with a smaller set of rules representing the actual fuzzy logic system. Copyright © 2007 John Wiley & Sons, Ltd.     

相邻建筑结构的模糊振动控制

本文研究了相邻建筑结构的模糊控制问题。首先,介绍相邻建筑结构体系的特点,建立体系的力学模型及运动方程;然后,进行了半主动控制研究,提出了控制的方法;最后,利用模糊控制方法实现了结构的智能控制。通过以上研究,说明相邻建筑结构相互振动控制是十分有效的,所得出的结论对实际工程的应用具有指导意义。     

Intelligent control for modeling of real‐time reservoir operation,part II: artificial neural network with operating rule curves

To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network‐based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M‐5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input–output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M‐5 curves in real‐time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved. Copyright © 2004 John Wiley & Sons, Ltd.     

Fuzzy sliding mode control for a building structure based on genetic algorithms

Based on the genetic algorithms (GAs), a fuzzy sliding mode control (FSMC) method for the building structure is designed in this research. When a fuzzy logic control method is used for a structural system, it is hard to get proper control rules directly, and to guarantee the stability and robustness of the fuzzy control system. Generally, the fuzzy controller combined with sliding mode control is applied, but there is still no criterion to reach an optimal design of the FSMC. In this paper, therefore, we design a fuzzy sliding mode controller for the building structure control system as an optimization problem and apply the optimal searching algorithms and GAs to find the optimal rules and membership functions of the FSMC. The proposed approach has the merit to determine the optimal structure and the inference rules of fuzzy sliding mode controller simultaneously. It is found that the building structure under the proposed control method could sustain in safety and stability when the system is subjected to external disturbances. Copyright © 2002 John Wiley & Sons, Ltd.     

基于区间直觉模糊数的地震应急服务点选址模型

地震应急物资储备服务点选址是地震应急救援决策工作的重要基础。本文分析了地震应急服务点选址问题的不确定性,介绍了直觉模糊数和区间直觉模糊数的概念,在分析两者之间关系的基础上,定义了区间直觉模糊数的得分函数和精确函数,进而提出了基于得分函数和精确函数的区间直觉模糊数的排序规则;行车时间受诸多因素影响,将行车时间看成区间直觉模糊信息,构建了约束中含有区间直觉模糊参数的地震应急服务点选址模型,提出了一种基于区间直觉模糊数排序规则的模型算法,可得到地震应急服务点最优选址方案。通过算例分析验证了该方法的有效性。     

基于自适应区间二型模糊聚类的遥感土地覆盖自动分类

遥感影像土地覆盖分类面临"类别密度差异显著"、"同谱异物"和"同物异谱"等不确定性问题,传统的分类方法(如FCM)因不能描述高阶模糊不确定性,无法完成准确建模,使分类误差较大,而二型模糊集恰是处理此类不确定性的有效工具.在引入二型模糊集新概念和自适应降型新方法的基础上,提出一种自适应二型模糊分类方法(A-IT2FCM):(1)基于样本集模糊距离度量构建面向分类的区间二型模糊集,以尽可能降低对先验知识和预设参数的依赖,从而满足自动分类的要求;(2)给出一种自适应探求等价一型代表(模糊)集合的高效降型方法,在此基础上进行自适应区间二型模糊聚类.实验数据为珠海横琴和北京颐和园的SPOT5影像数据,对比方法有AIT2FCM、基于Karnik-Mendel算法降型和基于Tizhoosh提出的简易降型方法的区间二型模糊C均值聚类以及作者前期研究提出的区间值模糊C-均值算法(IV-FCM).实验结果表明,A-IT2FCM方法分类效果佳,在类别具有较大密度差异和多重模糊性时能得到比FCM及IV-FCM更精确的边界和更连贯的类别,适于处理遥感影像土地覆盖类别的深层不确定性;同时在"光谱混叠"现象严重时,可以获得比对比方法更稳健、精度更高的影像自动分类结果,且时间复杂度明显低于基于Karnik-Mendel方法.     

New fuzzy neural network–Markov model and application in mid- to long-term runoff forecast

ABSTRACT

In this paper, a mid- to long-term runoff forecast model is developed using an ideal point fuzzy neural network–Markov (NFNN-MKV) hybrid algorithm to improve the forecasting precision. Combining the advantages of the new fuzzy neural network and the Markov prediction model, this model can solve the problem of stationary or volatile strong random processes. Defined error statistics algorithms are used to evaluate the performance of models. A runoff prediction for the Si Quan Reservoir is made by utilizing the modelling method and the historical runoff data, with a comprehensive consideration of various runoff-impacting factors such as rainfall. Compared with the traditional fuzzy neural networks and Markov prediction models, the results show that the NFNN-MKV hybrid algorithm has good performance in faster convergence, better forecasting accuracy and significant improvement of neural network generalization. The absolute percentage error of the NFNN-MKV hybrid algorithm is less than 7.0%, MSE is less than 3.9, and qualification rate reaches 100%. For further comparison of the proposed model, the NFNN-MKV model is employed to estimate (training and testing for 120-month-ahead prediction) and predict river discharge for 156 months at Weijiabao on the Weihe River in China. Comparisons among the results of the NFNN-MKV model, the WNN model and the SVR model indicate that the NFNN-MKV model is able to significantly increase prediction accuracy.

Editor D. Koutsoyiannis; Associate editor Y. Gyasi-Agyei     

Suspended load estimation using L1-fuzzy regression,L2-fuzzy regression and MARS-fuzzy regression models

ABSTRACT

The problem of estimation of suspended load carried by a river is an important topic for many water resources projects. Conventional estimation methods are based on the assumption of exact observations. In practice, however, a major source of natural uncertainty is due to imprecise measurements and/or imprecise relationships between variables. In this paper, using the Multivariate Adaptive Regression Splines (MARS) technique, a novel fuzzy regression model for imprecise response and crisp explanatory variables is presented. The investigated fuzzy regression model is applied to forecast suspended load by discharge based on two real-world datasets. The accuracy of the proposed method is compared with two well-known parametric fuzzy regression models, namely, the fuzzy least-absolutes model and the fuzzy least-squares model. The comparison results reveal that the MARS-fuzzy regression model performs better than the other models in suspended load estimation for the particular datasets. This comparison is done based on four goodness-of-fit criteria: the criterion based on similarity measure, the criterion based on absolute errors and the two objective functions of the fuzzy least-absolutes model and the fuzzy least-squares model. The proposed model is general and can be used for modelling natural phenomena whose available observations are reported as imprecise rather than crisp.

Editor D. Koutsoyiannis; Associate editor H. Aksoy     

Inverting reservoir parameters in a two-phase fractured medium with a niche genetic algorithm

In this paper we calculate a synthetic medium surface displacement response that is consistent with real measurement data by applying the least-square principle and a niche genetic algorithm to the parameters inversion problem of the wave equation in a two-phase medium. We propose a niche genetic multi-parameter (including porosity, solid phase density and fluid phase density) joint inversion algorithm based on a two-phase fractured medium in the BISQ model. We take the two-phase fractured medium of the BISQ model in a two-dimensional half space as an example, and carry out the numerical reservoir parameters inversion. Results show that this method is very convenient for solving the parameters inversion problem for the wave equation in a two-phase medium, and has the advantage of strong noise rejection. Relative to conventional genetic algorithms, the niche genetic algorithm based on a sharing function can not only significantly speed up the convergence, but also improve the inversion precision.     

River suspended sediment modelling using a fuzzy logic approach

Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating‐curve models. Benchmarking was based on a 5‐year period of continuous streamflow and sediment concentration data of Quebrada Blanca Station operated by the United States Geological Survey. The benchmark results showed that the fuzzy model was able to produce much better results than rating‐curve models. The fuzzy model proposed in the study is site specific and does not simulate the hysteresis effects. Copyright © 2006 John Wiley & Sons, Ltd.     

Seasonal Short‐Term Prediction of Dissolved Oxygen in Rivers via Nature‐Inspired Algorithms

This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R² parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R²), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models.     

Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces

Utilizing the rainfall intensity, and slope data, a fuzzy logic algorithm was developed to estimate sediment loads from bare soil surfaces. Considering slope and rainfall as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relations among rainfall intensity, slope, and sediment transport were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF-THEN format. The commonly used weighted average method was employed for the defuzzification procedure.The sediment load predicted by the fuzzy model was in satisfactory agreement with the measured sediment load data. Predicting the mean sediment loads from experimental runs, the performance of the fuzzy model was compared with that of the artificial neural networks (ANNs) and the physics-based models. The results of showed revealed that the fuzzy model performed better under very high rainfall intensities over different slopes and over very steep slopes under different rainfall intensities. This is closely related to the selection of the shape and frequency of the fuzzy membership functions in the fuzzy model.     

涡激振动下管桥段的模糊动力可靠性研究

本文首先给出了管道在流体作用下的力学模型，并对风力作用下管道产生涡激振动的机理进行了分析，从而建立了管桥在风力作用下的力学模型和相应的振动微分方程，同时给出了管桥的固有特性和动力响应分析结果，然后，在此基础上，提出了首超模糊失效、模糊疲劳失效和混合失效等三类模糊失效准则，并依据这些准则分析给出了动力可靠性的计算公式，最后，给出了具体的算例。