Self-Organizing Modeling in Forecasting Daily River Flows
نویسندگان
چکیده
An Artificial Neural Network is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. Such Neural Networks have been characterized by passive neurons that are not able to select and estimate their own inputs. In a new approach, which corresponds in a better way to the actions of human nervous system, the connections between several neurons are not fixed but change in dependence on the neurons themselves. This article presents a GMDH (Group Method of Data Handling) algorithm with active neurons. These neurons are able, during the learning or self-organizing process to estimate, which inputs are important to minimize the given objective function of the neuron. The nonlinear GMDH model approach is shown to provide better representation of the daily average water inflow forecasting, than the models based on Box-Jenkins method, currently in use on the Brazilian Electrical Sector.
منابع مشابه
A hybrid model of self organizing maps and least square support vector machine for river flow forecasting
Successful river flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and management. There are many forecasting techniques used for river flow forecasting. This study proposed a hybrid model based on a combination of two methods: Self Organizing Map (SOM) and Least Squares Support Vector Machine (LSSVM) model, referred to as the SOM-LSSVM mod...
متن کاملImproved streamflow forecasting using self-organizing radial basis function artificial neural networks
Streamflow forecasting has always been a challenging task for water resources engineers and managers and a major component of water resources system control. In this study, we explore the applicability of a Self Organizing Radial Basis (SORB) function to one-step ahead forecasting of daily streamflow. SORB uses a Gaussian Radial Basis Function architecture in conjunction with the Self-Organizin...
متن کاملDaily river flow forecasting in a semi-arid region using twodatadriven
Rainfall-runoff relationship is very important in many fields of hydrology such as water supply and water resourcemanagement and there are many models in this field. Among these models, the Artificial Neural Network (ANN) wasfound suitable for processing rainfall-runoff and opened various approaches in hydrological modeling. In addition,ANNs are quick and flexible approaches which provide very ...
متن کاملChaotic Analysis and Prediction of River Flows
Analyses and investigations on river flow behavior are major issues in design, operation and studies related to water engineering. Thus, recently the application of chaos theory and new techniques, such as chaos theory, has been considered in hydrology and water resources due to relevant innovations and ability. This paper compares the performance of chaos theory with Anfis model and discusses ...
متن کاملSelf Organizing Map and Least Square Support Vector Machine Method for River Flow Modelling Shuhaida Bte Ismail Universiti Teknologi Malaysia Self Organizing Map and Least Square Support Vector Machine Method for River Flow Modelling
Successful river flow time series forecasting is a primary goal and an essential procedure required in the planning and water resources management. River flow data are important for engineers to design, build and operate various water projects and development. The monthly river flow data taken from Department of Irrigation and Drainage, Malaysia are used in this study. This study aims to develo...
متن کامل