Abdüsselam Altunkaynak

Sediment load prediction by genetic algorithms

Accurate sediment load prediction is very important in planning, designing, operating and maintenance of water resources structures. Various models have been developed so far to identify the relation between discharge and sediment load. Most of the models based on regression method (RM) have some restrictive assumptions. This method is able to provide only one solution point for estimation of sediment amount. On the other hand, genetic algorithms (GAs) can produce more than one solution points providing optimal relation between discharge and sediment loads. Sediment load can be successfully predicted from discharge measurements by using GAs. Graphical and numerical data are presented to compare GAs with RM. GA methodology is applied to discharge and sediment load data obtained from Mississippi river at Missouri, St. Louis. It is found that GAs outperform RM in terms of mean relative error (MRE).

Comparative Case Study of Rainfall-Runoff Modeling between SWMM and Fuzzy Logic Approach

A comprehensive hydrological model, like the storm water management model (SWMM), has been widely used for rainfall-runoff simulation. In recent years, simple and effective modern modeling techniques have also brought great attention to the prediction of runoff with rainfall input. A comparative case study between SWMM and a presently developed fuzzy logic model for the predictions of total runoff within the watershed of Cascina Scala, Pavia in Italy is presented. A data set of 23 events from 2000 to 2003 including with the total rainfall and total runoff are adopted to train fuzzy logic parameters. Other data (1990–1995) with detailed time variations of rainfall and runoff are available for the setup and calibration of SWMM for runoff modeling. Among the 1990–1995 data, 35 independent rainfall events are selected to test the prediction performance of the SWMM and fuzzy logic models by comparing the predicted total runoffs with measured data. Comparisons and performance analyses in terms of the root-mean-squared error and coefficient of efficiency are made between the SWMM and the fuzzy logic model. The predicted total runoffs from either the SWMM or the fuzzy logic model are found to agree reasonably well with the measured data. For large rainfall events, the fuzzy logic model generally outperforms the SWMM unless the modification of the impervious ratio is applied to improve the SWMM results. However, the SWMM can produce the time varying hydrograph whereas fuzzy logic is subject to limitation of the methodology and is unable to generate such an output.

Wind Velocity Vertical Extrapolation by Extended Power Law

Wind energy gains more attention day by day as one of the clean renewable energy resources. We predicted wind speed vertical extrapolation by using extended power law. In this study, an extended vertical wind velocity extrapolation formulation is derived on the basis of perturbation theory by considering power law and Weibull wind speed probability distribution function. In the proposed methodology not only the mean values of the wind speeds at different elevations but also their standard deviations and the cross-correlation coefficient between different elevations are taken into consideration. The application of the presented methodology is performed for wind speed measurements at Karaburun/Istanbul, Turkey. At this location, hourly wind speed measurements are available for three different heights above the earth surface.

Fuzzy system modelling of drinking water consumption prediction

It is important to determine the amount of daily drinking water requirement for a person not only for the health of people but also for the planning and management of the water resources. Physical activity, body weight and temperature play significant role in drinking water consumption rates. Human activity variables are most often given in crisp numerical interval classifications for water consumption calculations. The aim of this paper is to establish a fuzzy model for predicting the water consumption rates based on data at the hand. The fuzzy sets such as low, medium, high can be used to quantify vague, imprecise or incomplete descriptions which are collectively referred to as fuzzy data in the literature. Fuzzy model inputs are considered as the physical activity, body weight and temperature, whereas the output is the water consumption levels. The fuzzy sets are chosen in an appropriate manner and the prediction model of water consumption is compared with the actual consumption amounts. It is not possible to treat such linguistic fuzzy data by statistical methods. It is observed that the model predictions have less than 5% relative error. The model is tested with an independent data set for its successful prediction capability.

Estimation of significant wave height in shallow lakes using the expert system techniques

Highlights? GKF approach has the advantages of Genetic and Kalman Filtering methods. ? GKF predicts better than ANN. ? This is the first time performances of GKF and ANN are examined for shallow water. ? The independent data set shows the importance of different seasonal conditions. ? Previous significant wave height and current wind speed affects current significant wave height. Significant wave height is an important hydrodynamic variable for the design application and environmental evaluation in coastal and lake environments. Accurate prediction of significant wave height can assist the planning and analysis of lake and coastal projects. In this study, the Genetic Algorithm (GA) is used as the optimization technique to better predict model parameters. Also, Kalman Filtering (KF) is used for prediction of significant wave height from wind speed. KF technique makes predictions based on stochastic and dynamic structures. The integrated Geno Kalman Filtering (GKF) technique is applied to develop predictive models for estimation of significant wave height at stations LZ40, L006, L005 and L001 in Lake Okeechobee, Florida. The results show that the GKF methodology can perform very well in predicting the significant wave height and produce lower mean relative error and mean-square error than those from Artificial Neural Network (ANN) model. The superiority of GKF method over ANN is presented with comparisons of predicted and observed significant wave heights.

A predictive tool by fuzzy logic for outcome of patients with intracranial aneurysm

We aimed to investigate if the outcome of the patients with intracranial aneurysm could be predicted by fuzzy logic approach. Two hundred and forty two patients with the diagnosis of intracranial aneurysm were assessed retrospectively between January 2001 and December 2005. We recorded World Federation of Neurological Surgeons Scale (WFNSS), Fisher Scale and age at admission and Glasgow Outcome Score (GOS) at discharge from hospitalization for all the patients. We developed fuzzy sets by dividing WFNSS into four groups as good, fair, bad and very bad; age into three groups as young, middle and old; Fisher scale into three groups as few, moderate and large; outcome score into four groups as bad, fair, good and very good. We calculated the outcome of the patient with these sets by fuzzy model. Predicted outcome by fuzzy logic approach correlated with observed outcome scores of the patients (p>0, 05), including 95% confidence interval. We showed that outcome of the patients with aneurysm can be predicted by fuzzy logic approach, accurately.

Suspended sediment concentration prediction by Geno-Kalman filtering

More accurate prediction of suspended sediment concentration will likely lead to more economic hydraulic construction and provide a valuable basis for the optimum operation of water resources. The majority of past models have relied on simple regression analysis relating discharge to concentration. A new adaptive prediction approach termed Geno-Kalman filtering (GKF), combining Genetic Algorithm and Kalman filtering techniques is proposed. The model is formed in three steps. Firstly, discharge and suspended sediment concentration are related by using dynamic linear model. Secondly, an optimum transition matrix relating these two state variables is obtained by Genetic Algorithms (GAs), and an optimum Kalman gain is calculated. Thirdly, Kalman filtering is used to predict the suspended sediment concentration from discharge measurement. The proposed method is applied to measurements at the Mississippi River basin in St. Louis, Missouri, and is found to result in smaller absolute, mean square, relative errors compared to perceptron Kalman filtering. Furthermore, Geno-Kalman filtering method outperforms the perceptron Kalman filtering and least square methods in terms of coefficient of efficiency.

Fuzzy logic-based attenuation relationships of strong motion earthquake records

A new fuzzy-logic based model is developed for attenuation relationships of earthquake records.Two fuzzy sets are defined for the epicentral distance and the earthquake magnitude.The model results in a higher coefficient of efficiency when compared to available physical models. Fuzzy logic techniques have been widely used in civil and earthquake engineering applications in the past four decades. However, no thorough research studies were conducted to use them for deriving attenuation relationships for peak ground accelerations (PGA). This paper is an attempt to fill this gap by employing a fuzzy approach with fuzzy sets for earthquake magnitude and distance from source with the objective of proposing new ground motion attenuation models. Recent earthquake records from USA and Taiwan with magnitudes 5 or greater were used; and consisted of horizontal peak ground acceleration recorded on three different site conditions: rock, soil and soft soil. The use of Fuzzy models to quantify ground motion records, which are typically characterized by a high level of uncertainty, leads to a rational analytical tool capable of predicting accurate results. Testing of the fuzzy model with an independent data set confirmed its accuracy in predicting PGA values.

Steady-state groundwater flow model with variable hydraulic conductivity

Abstract Classical aquifer test models assume an isotropic and homogenous medium with Darcian flow as an ideal case. Deviations from type curves indicate the heterogeneity of the aquifer. There are heterogeneities even at small scales. There are also systematic variations which are not considered by type curves. For instance, due to the groundwater movement during the well-development phase, the hydraulic conductivity tends to decrease with radial distance from the well. For practical representation of such a systematic variation, a linear hydraulic conductivity decrease is adopted and the relevant type curve expressions are derived. These expressions are checked against the classical constant hydraulic conductivity solutions in the literature. Derived type curves are employed for the identification of aquifer parameters, namely transmissivity and the radial hydraulic conductivity variation parameters. The type curve expression derived transforms into the classical Thiem expression when the aquifer hydraulic conductivity is considered as constant. It is observed that classical steady-state flow with constant hydraulic conductivity underestimates the transmissivity by 10%.

Comparison of Discrete and Continuous Wavelet–Multilayer Perceptron Methods for Daily Precipitation Prediction

AbstractWavelet transforms are combined with predictive methods to develop prediction approaches so that the prediction accuracy can be improved in hydrologic predictions. Although the wavelet transform generates several subseries that show similar characteristics, the predictive method is used to develop the model using those subseries. There are several examples of these kinds of combined models, such as wavelet–multilayer perceptron (MP), wavelet fuzzy, wavelet autoregressive, and so forth. Generally, discrete wavelet transformation is used in combined models rather than continuous wavelet transform for unexplained reasons. As a result, in this study emphasis was placed on the comparison of the continuous wavelet–multilayer perceptron (CWT-MP) and discrete wavelet–multilayer perceptron (DWT-MP) models, which were also compared with the stand-alone MP model. Daily precipitation time series from two stations were used in the model development and comparison process. The current precipitation values were ...