Yalnızca bulanık mantık modellemesi ve bu çalışmada önerdiğimiz bulanık mantık ve dalgacık analizi kombinasyonu modeli ile de **sediment** taşınımı tahmini yapılmıştır. Bulanık mantık modelinde Takagi-Sugeno bulanık sistemi kullanılmıştır. Gauss tipi ve üçgen tipi bulanık üyelik fonksiyonları modelleme için seçilmiştir. Üyelik fonksiyonlarının seçimi modelimiz tarafından deneme-yanılma yoluyla bulunmuştur. Bulanık mantık yönteminin tek başına uygulandığı durumda korelasyon katsayılarının oldukça düşük çıktığı görülmüştür, buna ek olarak ortalama mutlak hatalarda oldukça yüksek çıkmıştır. **Sediment** taşınımını, dalgacık analizi ve bulanık mantık kombinasyonuyla incelediğimiz durumda, korelasyon katsayılarının oldukça yüksek sonuçlar verdiği görülmüştür. Önerilen yöntemle 0,94 ile 0,99 arasında değişen korelasyon değerleri elde edildiği görülmüştür. Bunun yanı sıra diğer metotlar ile karşılaştırıldığı zaman ortalama mutlak hata değerleri oldukça minimize olduğu görülmüştür. Önerdiğimiz modelin dalgacık dönüşümü kısmında, sürekli dalgacık analizi uygulanmıştır. Sürekli dalgacık dönüşümü, dalgacık formunun kaydırılıp ölçekle çarpılıp, sonrasında zaman alanı boyunca toplanması olarak tanımlanır. Sürekli dalgacık fonksiyonu uygulandığında sinyalin farklı bölgelerinde farklı ölçeklerde katsayı elde ediyor. Bu katsayılar orijinal **sediment** yükü zaman serisinin regresyon sonucunu gösterir. Dalgacık analizi kısmında, Meksika şapkası dalgacık formu kullanılmıştır. Bulanık mantık kısmı, aynı sadece bulanık mantık modeli oluşturulacak gibi yeniden Gauss ve üçgen tipi üyelik fonksiyonlarından biri seçilmiş, bulanıklaştırma yapılmış ve sonrasında yeniden birleştirme yapılarak sonuçlar elde edilmiştir.

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frequency bands and elimination of noise. The approach may be that one can use only significant frequencies in the **prediction** scheme to obtain more accurate results. Webster and Hoyos (2004) suggested the use of signifi- cant variances in the **wavelet** spectra for the separation of frequency bands. The PMDI time series (Fig. 1a), which is considered as predictand, and its corresponding continuous **wavelet** transform along with the **wavelet** spectra are shown in Figs. 1b and 1c, respectively. It is evident from the figure that there are six distinct fre- quency bands, which are 7–16, 17–33, 34–56, 57–93, 94– 222, and .223 months. The time series of **wavelet** bands are obtained by inverse **wavelet** filtering (Fig. 2). The Morlet **wavelet** is employed for **wavelet** analysis. The frequency bands obtained from the **wavelet** transform of predictand are used for other predictors. There are six bands that should be predicted from their corresponding predictors. As a final step, these predicted bands are reconstructed to establish the desired PMDI time series. c. **Fuzzy** **logic**

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The capability of ANNs to find nonlinear relations between inputs and outputs make them proper tools for modelling hydraulic and hydrological phenomena [7] .The ANN simulation has been increasingly applied in many countries, and its use for time series modelling has recently been discovered. The **wavelet**-transformed data of observed time series enhance forecasting capabilities by capturing valuable information at different resolution levels [8] . The performance of multi layer feed forward (MLFF) network, and radial basis function (RBF) network, to forecast the discharge of **suspended** sediments has been compared [9] . The ANN employment in support of river SS **prediction** has been studied by many researchers [10, 11] . The ANN, neuro-**fuzzy** (NF), multi layer regression (MLR) and **sediment** rating curve (SRC) models were examined for the one day ahead simulation of SS in two hygrometry stations. It was established by comparison of modelling results that the NF **model** is better suited for the SS forecasting [12] . The ANN **model** was proposed as a means for simulating the monthly **suspended** **sediment** flux in China [9] . In the mentioned **model**, the **suspended** **sediment** flux was correlated with the temperature, average rainfall, rainfall intensity, and flow discharge. Results show that the ANN **model** is capable of simulating the monthly **suspended** **sediment** flux [13] . Other investigators have defined a **model** by combining the **wavelet** transform and the neuro-**fuzzy** (NF) technique to predict the daily **suspended** **sediment** [14] . The ANN approach was used to **model** the SS concentration on two sites on the Mississippi River. The corresponding results have revealed that the ANN technique is better when compared to conventional methods [15] .

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In another approaches, **wavelet** analysis and NF were applied to daily **suspended** **sediment** **load** **prediction** in a gauging station in the USA. In the WNF **model**, selection of appropriate decomposed time series was important in the **model** performance. Afterwards, these total time series were imposed as inputs into the NF **model** for SS **prediction** in one day ahead [8]. The support vector machine (SVM) was a supervised learning method that generates input-output mapping functions from a set of labeled training data [9]. In training support vector machines the decision boundaries were determined directly from the training data so that the separating margins of decision boundaries were maximized in the high-dimensional space called feature space. This learning strategy is based on statistical learning theory and minimizes the classification errors of the training data and the unknown data [10].

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Azamathulla et al. (2012) state four basic operators in gene expression programming (GEP) as alternative approach to modelling the **suspended** **sediment** **load** of river systems. They also used adaptive neuro-**fuzzy** inference system (ANFIS), regression **model** together with GEP to predict the **suspended** **sediment**. The data provided by REDAC with total 214 sets of data from three different rivers in Malaysia were used in this study. The result from different **model** will be compared and obtained the best performance by **using** RMSE, R 2 and average error (AE). Even ANFIS predicting accurate value of SSC but GEP **model** is suggested for preliminary **prediction** due to the complexity of ANFIS **model** since the traditional formulas fail to predict the **suspended** **sediment** **load** accurately. ANFIS has been suggested to be used to predict the SSC in the future.

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As the civilization is developing the problem of traffic on the road is increasing on daily basis. To help people tackle with traffic problems the best option is to know the situation of traffic in advance, so people can manage their work and save their time by adjusting their daily schedule of traveling. In thesis proposal, we presented a time series forecasting based on the **combination** of **Fuzzy** **logic**, **wavelet** analysis, and ARIMA. We have shown how the **fuzzy** **logic** is built to categorize data to provide more accurate data in the **prediction** **model**. The results state that if data is classified then better accuracy can be achieved. Usually time series ARIMA forecasting **model** uses past data with lagged value. **Fuzzy** **logic** in this **model** is used to build the dataset by considering only those past data which are most relevant to the **prediction** duration. For example, if the day is Monday and season is winter, then only those data with higher frequency matching will be selected which helps to get the more fitted **model** in ARIMA process. The multi-scaling property of the DWT enhances the **prediction** of non-linear and denoised traffic time series, and finally, ARIMA is used as a **prediction** **model** for long-term as well as short-term traffic **prediction**. For long-term traffic **prediction**, we have used sharp variation points to examine the detail level of traffic fluctuation to forecast for a certain period.

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The **suspended** **load** transport rate was computed for the data set **using** the three selected formulas and then compared with those of the measured values. The correlation coefficient, discrepancy ratio and relative error were used for the comparison of performance. The accuracy order was prepared on the basis of data coverage between the discrepancy ratio of (0.5-2.0), the Min. relative error and the calculated values were plotted against the observed values so that the scatter about the perfect agreement line can also be considered.

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The basic concept of **fuzzy** set theory was ﬁ rst introduced by Zadeh in 1965 [6]. **Fuzzy** set theory can be considered as a generalized classical set theory. Normally, in classical set theory an element can either belong to a particular set or not. Therefore, the degree of being a member of that set is its crisp value. How- ever, in **fuzzy** set theory, the degree of membership of an element can be continuously varied. **Fuzzy** set maps from the universe of discourse to the close interval {0, 1} [17]. The continuous nature of data can be represented by a membership function in **fuzzy** sets. **Fuzzy** set theory is one of the dominant technologies in arti ﬁ cial intelligence (AI) and it has broad application in **load** forecasting. For example, it can **model** ordinary linguistic variables which may be imprecise or vague in nature at a cognitive level [1,7]. **Load** forecasting involves many uncertainties, such as the variation in such factors as temperature, humidity, rainfall, wind speed, at- mospheric pressure and solar radiation with respect to **load**, and its value cannot be exactly determined numerically [10]. There- fore, a **fuzzy** **logic** approach will be the most suitable method to use under such conditions.

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XML format is widely used to create most web pages [10]. The payload could hence be obtained in the XML format. The Document Object **Model** (DOM) is the foundation of Extensible Mark-up Language, or XML. XML documents have a hierarchy of informational units called nodes [11]. The XML DOM (Document Object **Model**) defines a standard way for accessing and manipulating XML documents. The DOM presents an XML document as a tree structure, with elements, attributes, and text as nodes. Information from the web pages is obtained by parsing the xml document. A database is formed with the parsed data. An analysis of the database provides a picture of the variations in the market due to the numerous available factors ranging from economical to political factors. All these factors can be finally distilled into one market variable, the stock market price.

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Abstract The key atmospheric variables that impact crops are weather and rainfall. Extreme rainfall or drought at critical periods of a crop’s development can have dramatic influences on productivity and yields. The analysis of effect of rainfall is needed to evaluate crop production in Northeastern Thailand. Two operations were performed on the **Fuzzy** **Logic** **model**; the fuzzification operation and defuzzification operation. The **model** predicted outputs were compared with the actual rainfall data. Simulation results reveal that predicted results are in good agreement with measured data. **Prediction** Error and Root Mean Square Error (RMSE) were calculated, and on the basis of the results obtained, it can be suggested that **fuzzy** methodology is efficiently capable of handling scattered data.

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A **fuzzy** inference system (FIS) is a system that uses **fuzzy** set theory to map inputs to outputs. FIS is mainly of two types Mamdani FIS and Sugeno FIS. Mamdani FIS theory was proposed in 1975 by Ebrahim Mamdani to control a steam engine and boiler **combination** by mixing a set of linguistic control rules in the form if then rule which are acquired from the experience of human operators [11].

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In order to validate the **suspended**-**sediment** concentration in the TFL, the monthly measured data collected from November 2009 to July 2011 was compared with the simulated results. The water sample was taken from different water depths to measure the concentration of **suspended** **sediment**. Concentrations of **suspended** **sediment** were determined **using** the drying method after filtering samples through GF/F filters [43]. The comparison of the simulated **suspended**-**sediment** concentration and the measured concentration taken as a vertical average at the buoy station is shown in Figure 4. It reveals that simulated results fairly match the measured **suspended**-**sediment** concentration. The MAE and RMSE values between the computed and measured **suspended**-**sediment** concentrations are 1.73 mg/L and 2.23 mg/L, respectively.

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Quality index method (QIM) is the best way to detect the freshness of the fish which use the characteristic of eyes, gills and skin of the fish. The scope of this work is to construct a method to test the freshness of the fish based on image processing, wavelets decomposition and **fuzzy** **logic**. Image analysis is a non- destructive, harmless common tool for evaluating data based on photography and analysis of its color variations through imaging software can be an important method to validate the quality of fish. Image processing can help to

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After a brief review of the theoretical basis of the hydrodynamic characteristics of two-dimensional depth-averaged flow in a horizontal plane, in this paper we present an equation for **suspended** **sediment** transport. It is an advective-diffusion equation with an added source term that describes the concentration of a **suspended** **sediment** caused by sedimentation or erosion. The depth-averaged concentration of the **suspended** **load** is a result of an analysis of the transport equation in the vertical plane. The source-term definition is based on the transport equation in the vertical plane, which gives a characteristic concentration distribution of the **suspended** **load** with a minimum concentration at the surface and a maximum at the bottom of the bed. The calculation results depend on the difference between the inflow (calculated), depth-averaged concentration of the suspension and the averaged equilibrium suspension concentration in a numeric cell under certain hydrodynamic conditions. As an example of the application of the mathematical **model**, the problem of Ptuj lake (Slovenia) is presented. It is very exposed to the sedimentation of **suspended** **sediment** that is brought by the river Drava. The results of the measurements, the procedure of the hydrodynamic part of the mathematical **model** calibration and the results of the **suspended**-**load** module are presented. © 2003 Journal of Mechanical Engineering. All rights reserved.

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Till date, several researchers have dealt with the application of various neural networks to short term **load** forecasting with varying suc- cess ( Lee at al., 1992; Chen et al., 1992; Lu et al., 1993; Ranaweera, et al., 1995; Bakirtzis et al., 1996; Lamedica et al., 1996, Beccali et al., 2001; Topalli et al., 2003; Carpinteiro et al., 2004; Satish et al., 2004; Topalli et al., 2006 ) . Although neural networks are capable of han- dling nonlinearity between the electric **load** and the weather factors that affect the **load**, they lack to handle unusual changes that occur in the environment. **Fuzzy** **logic** is often an effective approach to these uncertainties. **Fuzzy** **logic**- based systems were found to perform well in a dynamically changing environment. Srinivas et al. ( 2002 ) discussed various applications of **Fuzzy** **logic**. Kyung-Bin Song et al. ( 2005 ) de- veloped a new **fuzzy** linear regression method for short term 24 hourly loads forecasting of the holidays. The concept of **fuzzy** regression analysis is employed for STLF. The **fuzzy** lin- ear regression **model** is made from the historical data and coefficients of the **model** are solved by mixed linear programming problem. Tranchita

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ments had identical instrumentation deployed for temporally high-resolution nutrient, conductivity, temperature and tur- bidity data capture **using** bankside analysers mains powered at 230 V (Fig. 2; Wall et al., 2011; Jordan et al., 2012; Mel- land et al., 2012b). Turbidity (T) data were collected us- ing a turbidity sensor (Solitax, Hach-Lange, Germany; range 0–4000 NTU; factory calibrated to 1000 NTU) and SC1000 controller at 10 min intervals. The sensors were located out- of-stream (ex situ) in a rapidly and continuously circulating header tank with river water delivered from the channel by an in-stream pump (30 m 3 h −1 ) located on the channel bed. The instrument tank was assumed well mixed as no partic- ulate deposition occurred. Turbidity probes were fitted with wipers to prevent biological fouling, and checked monthly against deionised water (0 NTU) and a 20 NTU Formazin tur- bidity standard. Synchronised discharge data (Q – m 3 s −1 ) were calculated from vented pressure-transducer stage mea- surements (OTT Orpheus-mini; OTT Germany). Stage height was converted to Q **using** velocity-area measurements (OTT Acoustic Doppler Current meter; OTT Germany) collected over non-standard flat-v weirs (custom made, Corbett Con- crete, Ireland) and WISKI-SKED software (Grassland A, R 2 = 0.96, n = 272; Grassland B, R 2 = 1, n = 166 (Mellan- der et al., 2015); Grassland C, R 2 = 0.95 and 0.97, n = 316; Arable A, R 2 = 1, n = 376 (Mellander et al., 2015); Arable B, R 2 = 0.94 and 1, n = 493). Both Grassland C and Arable B had changing controls at higher discharges and WISKI- SKED provided two parts to the curves with two R 2 coeffi- cients.

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Work has the ability to identify true worth of stock. This **model** involves the strength of **Fuzzy** **Logic** to expect stock price. System considers fundamental concern for study. Methodology gives indistinguishable value for stock. It becomes inconsequential to contrast that whether the supposing stock is value to contemporary price or not. Long term **prediction** is the base for the algorithm. Accuracy of the algorithm is 0.77. System focuses on the standard parameters to determine true worth. The current system is designed for stocks. Bonds and other financial instruments may be used for future work.

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functions are used to convert these partial belonging data to 0 to 1 range. To assign membership functions to **fuzzy** variables di ﬀerent approaches are used, such as, inductive reasoning, genetic algorithms, neural networks, inference, intuition, angular **fuzzy** sets, and rank ordering. These membership functions may take numerous structures, but generally triangular ones are in use because of it is a simple linear function.

In this study, an approach to predict the surface of cutting quality has been identified by **using** the **fuzzy** techniques. Today’s industry is seeking for the best machine that can produce high quality of the cutting machine. Furthermore, this cutting machine is an advanced thermal cutting process of complex nature. Thin film coating is the thin layer of materials that are used to develop filters, increase insulation or conduction, protect them from lights or create reflective surfaces. Therefore by applying thin film coating on cutting tool the performance of the cutting tool will be increase and have the improvement (Jaya 2013).

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Abstract— Emotional agents are useful to variety of com- puter applications. This paper focuses on the emotion of surprise. Surprise can be considered as the automatic reaction to a mismatch, which plays an important role in the behaviors of intelligent agents. We represent psychological theories of surprise through **fuzzy** inference systems, as **fuzzy** **logic** helps to capture the **fuzzy** and complex nature of emotions. We infer the degree of surprise from four factors relating to it by three kinds of **fuzzy** inference systems respectively, and propose **fuzzy** inference rules as well as reasoning parameters for the systems. Case study shows the surprise generation process by **fuzzy** inference system. The surprise inference system can be applied into the decision making process of agents in uncertain environments.

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