Artificial Neural Network (ANN) is an information-processing system composed of many nonlinear and densely interconnected processing elements or neurons. ANN is able to extract the relation between the inputs and outputs of a process, without the physics being explicitly provided to them. The natural behavior of hydrological processes is appropriate for the application ANN in hydrology. A rainfall runoff model for Sungai Bedup Basin in Sarawak was built using three different ANN architectures namely Multilayer perceptron (MLP), Recurrent (REC) and Radial Basic function (RBF)
The concept of rainfall-runoff variation is a non-linear event and highly tedious and continuously c...
A study investigating the forecast of runoff for an overland flow using the artificial neural networ...
Rainfall-runoff modeling is one of the most studied topics in hydrology. Various types of intelligen...
Rainfall-runoff relationships are among the most complex hydrologic phenomena. Hydrologists have dev...
The Artificial Neural Network (ANN) is a method of computation inspired by studies of the brain and ...
The Artificial Neural Network (ANN) is a method of computation inspired by studies of the brain and ...
Rainfall-runoff relationships are among the most complex hydrologic phenomena. The conceptual models...
Rainfall and surface runoff are the driving forces behind all stormwater studies and designs. The re...
The use of an artificial neural network (ANN) is becoming common due to its ability to analyse compl...
This study proposes the application of Artificial Neural Network in the modelling hourly runoff for ...
Abstract. The modelling of hydraulic and hydrological processes is important in view of the many use...
Neural network is a very useful data modelling tool that is able to capture and represent complex in...
Background/Objective: The main objective of the present study is to conduct laboratory experiment fo...
Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understandin
This study aims to improve water level prediction at Bedup River with estimations made to absent pre...
The concept of rainfall-runoff variation is a non-linear event and highly tedious and continuously c...
A study investigating the forecast of runoff for an overland flow using the artificial neural networ...
Rainfall-runoff modeling is one of the most studied topics in hydrology. Various types of intelligen...
Rainfall-runoff relationships are among the most complex hydrologic phenomena. Hydrologists have dev...
The Artificial Neural Network (ANN) is a method of computation inspired by studies of the brain and ...
The Artificial Neural Network (ANN) is a method of computation inspired by studies of the brain and ...
Rainfall-runoff relationships are among the most complex hydrologic phenomena. The conceptual models...
Rainfall and surface runoff are the driving forces behind all stormwater studies and designs. The re...
The use of an artificial neural network (ANN) is becoming common due to its ability to analyse compl...
This study proposes the application of Artificial Neural Network in the modelling hourly runoff for ...
Abstract. The modelling of hydraulic and hydrological processes is important in view of the many use...
Neural network is a very useful data modelling tool that is able to capture and represent complex in...
Background/Objective: The main objective of the present study is to conduct laboratory experiment fo...
Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understandin
This study aims to improve water level prediction at Bedup River with estimations made to absent pre...
The concept of rainfall-runoff variation is a non-linear event and highly tedious and continuously c...
A study investigating the forecast of runoff for an overland flow using the artificial neural networ...
Rainfall-runoff modeling is one of the most studied topics in hydrology. Various types of intelligen...