Support vector machine approach for longitudinal dispersion coefficients in natural streams
نویسندگان
چکیده
This paper presents the support vector machine approach to predict the longitudinal dispersion coefficients in natural rivers. Collected published data from the literature for the dispersion coefficient for wide range of flow conditions are used for the development and testing of the proposed method. The proposed SVM approach produce satisfactory results with coefficient of determination=0.9025 and root mean square error =0.0078 compared to existing predictors for dispersion coefficient. © 2010 Elsevier B.V. All rights reserved.
منابع مشابه
Application of genetic algorithm (GA) to select input variables in support vector machine (SVM) for analyzing the occurrence of roach, Rutilus rutilus, in streams
Support vector machine (SVM) was used to analyze the occurrence of roach in Flemish stream basins (Belgium). Several habitat and physico?chemical variables were used as inputs for the model development. The biotic variable merely consisted of abundance data which was used for predicting presence/absence of roach. Genetic algorithm (GA) was combined with SVM in order to select the most important...
متن کاملPrediction of Longitudinal Dispersion Coe cient in Natural Channels Using Soft Computing Techniques
Accurate estimate of longitudinal dispersion coe cient is essential in many hydraulic and environmental problems such as intake designs, modeling ow in esturies and risk assessment of injection of hazardous pollutants into river ows. Recent research works show that in the absence of knowledge about explicit relationships concerning longitudinal dispersion coe cient and its in uencing parameters...
متن کاملApplication of multilayer perceptron neural network and support vector machine for modeling the hydrodynamic behavior of permeable breakwaters with porous core
In this research, the application of multilayer perceptron (MLP) neural networks and support vector machine (SVM) for modeling the hydrodynamic behavior of Permeable Breakwaters with Porous Core has been investigated. For this purpose, experimental data have been used on the physical model to relate the reflection and transition coefficients of incident waves as the output parameters to the wid...
متن کاملApplication of Genetic Algorithm Based Support Vector Machine Model in Second Virial Coefficient Prediction of Pure Compounds
In this work, a Genetic Algorithm boosted Least Square Support Vector Machine model by a set of linear equations instead of a quadratic program, which is improved version of Support Vector Machine model, was used for estimation of 98 pure compounds second virial coefficient. Compounds were classified to the different groups. Finest parameters were obtained by Genetic Algorithm method ...
متن کاملMODELING OF FLOW NUMBER OF ASPHALT MIXTURES USING A MULTI–KERNEL BASED SUPPORT VECTOR MACHINE APPROACH
Flow number of asphalt–aggregate mixtures as an explanatory factor has been proposed in order to assess the rutting potential of asphalt mixtures. This study proposes a multiple–kernel based support vector machine (MK–SVM) approach for modeling of flow number of asphalt mixtures. The MK–SVM approach consists of weighted least squares–support vector machine (WLS–SVM) integrating two kernel funct...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Appl. Soft Comput.
دوره 11 شماره
صفحات -
تاریخ انتشار 2011