Surface Water Quality Evaluation Using BP and RBF Neural Network
نویسندگان
چکیده
It is very important to evaluate water quality in environment protection. Water environment is a complicated system, traditional methods cannot meet the demands of water environment protection. In view of the deficiency of the traditional methods, a BP neural network model and a RBF neural network model are proposed to evaluate water quality. The proposed model was applied to evaluate the water quality of 10 sections in Suzhou River. The evaluation result was compared with that of the RBF neural network method and the reported results in Suzhou river. It indicated that the performance of proposed neural network model is practically feasible in the application of water quality assessment and its operation is simple.
منابع مشابه
Evaluating Seepage of Dam Body Using RBF and GFF Models of Artificial Neural Network
Dams have been always considered as the important infrastructures and their critical values are counted. Hence, evaluation and avoidance of dams’ destruction have a specific importance. Seepage occurrence in dams is an inevitable phenomenon. Despite all the progress in geotechnical engineering, up to now, seepage problem is the main conflict which occurs in dams. This study tried to estimate se...
متن کاملShort-term prediction of atmospheric concentrations of ground-level ozone in Karaj using artificial neural network
Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level...
متن کاملShort-term prediction of atmospheric concentrations of ground-level ozone in Karaj using artificial neural network
Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level...
متن کاملNEURAL NETWORK-BASED RELIABILITY ASSESSMENT OF OPTIMALLY SEISMIC DESIGNED MOMENT FRAMES
In the present study, the reliability assessment of performance-based optimally seismic designed reinforced concrete (RC) and steel moment frames is investigated. In order to achieve this task, an efficient methodology is proposed by integrating Monte Carlo simulation (MCS) and neural networks (NN). Two NN models including radial basis function (RBF) and back propagation (BP) models are examine...
متن کاملError Modeling in Distribution Network State Estimation Using RBF-Based Artificial Neural Network
State estimation is essential to access observable network models for online monitoring and analyzing of power systems. Due to the integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on the possible correlation between the real and...
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:
- JSW
دوره 6 شماره
صفحات -
تاریخ انتشار 2011