Top PDF Suspended Sediment Load Prediction By Using Wavelet-fuzzy Logic Combination Model

Suspended Sediment Load Prediction By Using Wavelet-fuzzy Logic Combination Model

Suspended Sediment Load Prediction By Using Wavelet-fuzzy Logic Combination Model

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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Long Lead Time Drought Forecasting Using a Wavelet and Fuzzy Logic Combination Model: A Case Study in Texas

Long Lead Time Drought Forecasting Using a Wavelet and Fuzzy Logic Combination Model: A Case Study in Texas

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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Suspended sediment modelling by SVM and wavelet

Suspended sediment modelling by SVM and wavelet

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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A Wavelet Support Vector Machine Combination Model for Daily Suspended Sediment Forecasting

A Wavelet Support Vector Machine Combination Model for Daily Suspended Sediment Forecasting

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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Numerical Modelling for Prediction of Suspended Sediment Concentration in Bidor River

Numerical Modelling for Prediction of Suspended Sediment Concentration in Bidor River

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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AN APPROACH OF TRAFFIC FLOW PREDICTION USING ARIMA MODEL WITH FUZZY WAVELET TRANSFORM

AN APPROACH OF TRAFFIC FLOW PREDICTION USING ARIMA MODEL WITH FUZZY WAVELET TRANSFORM

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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Multiple Linear Regression Model for Suspended Load Transport Rate Prediction and Its Evaluation Using Selected Transport Formulas

Multiple Linear Regression Model for Suspended Load Transport Rate Prediction and Its Evaluation Using Selected Transport Formulas

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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Long-term load forecast modelling using a fuzzy logic approach

Long-term load forecast modelling using a fuzzy logic approach

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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A Prediction System Based on Fuzzy Logic

A Prediction System Based on Fuzzy Logic

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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Statistics Model for Meteorological Forecasting Using Fuzzy Logic Model

Statistics Model for Meteorological Forecasting Using Fuzzy Logic Model

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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Exercise Bike Model using Fuzzy Logic

Exercise Bike Model using Fuzzy Logic

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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Investigating Suspended-Sediment Transport in a Shallow Lake Using a Three-Dimensional Model

Investigating Suspended-Sediment Transport in a Shallow Lake Using a Three-Dimensional Model

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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FISH FRESHNESS CLASSIFICATION USING WAVELET TRANSFORMATION AND FUZZY LOGIC TECHNOLOGY

FISH FRESHNESS CLASSIFICATION USING WAVELET TRANSFORMATION AND FUZZY LOGIC TECHNOLOGY

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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A Two-Dimensional Mathematical Model of Suspended-Sediment Transport

A Two-Dimensional Mathematical Model of Suspended-Sediment Transport

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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Short Term Electric Load Prediction Using Fuzzy BP

Short Term Electric Load Prediction Using Fuzzy BP

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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Investigating suspended sediment dynamics in contrasting agricultural catchments using ex situ turbidity based suspended sediment monitoring

Investigating suspended sediment dynamics in contrasting agricultural catchments using ex situ turbidity-based suspended sediment monitoring

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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Fuzzy Logic based Stock Value Prediction using Fundamental Analysis

Fuzzy Logic based Stock Value Prediction using Fundamental Analysis

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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A Web Service Reliability Prediction Using HMM and Fuzzy Logic Models

A Web Service Reliability Prediction Using HMM and Fuzzy Logic Models

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.

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Prediction of TiN film coating characteristics using fuzzy logic techniques

Prediction of TiN film coating characteristics using fuzzy logic techniques

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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Fuzzy Logic Model of Surprise

Fuzzy Logic Model of Surprise

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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