Multilayer Feedforward Network Model Research Articles

Sediment transport modelling in an alluvial river with artificial neural network

Simulation of flow and transport of sediment in alluvial rivers, particularly during release of water through dams, is a complex problem. Such simulations are generally carried out using various numerical models, subject to availability of requisite morphological and sediment data sets. In addition, various simplifying assumptions, quite often, are part and parcel of most of the available numerical sediment transport models. As such, non-compliance of any of the assumptions or non-availability of all the requite data sets restricts meaningful use of any numerical simulator in estimation of morphological changes in alluvial rives. This study attempts to resolve such issues associated with numerical models by using Artificial Neural Networks (ANN). The primary objective of the study is to explore and identify best performing ANN models in estimating morphological changes in alluvial rivers. In the present study, the one dimensional hydrodynamic modelling system, HEC-RAS (Hydrologic Engineering Centre’s River Analysis System), is employed for generating database to train multilayer feedforward networks (MLFN). Performance evaluations of all the developed MLFN models are made by using three statistical parameters, i.e. Mean Square Error (MSE), Mean Absolute Error (MAE) and Coefficient of Determination (R2). The study reveals that there are multiple ANN models, stretching across different training algorithms and transfer functions, which can be reliably used in developing sediment transport models in alluvial rivers.

Multilayer Feed Forward Models in Groundwater Level Forecasting Using Meteorological Data in Public Management

Managing the groundwater resources is very vital for human life. This research proposes a methodology for predicting the groundwater levels which can be very valuable in water resources management. This study investigates the application of multilayer feed forward network models for forecasting the groundwater values in the region of Montgomery country in Pennsylvania. Multiple training algorithms and network structures were investigated to develop the best model in order to forecast the groundwater levels. Several multilayer feed forward models were created in order to be tested for their performance by changing the network topology parameters so as to find the optimal prediction model. The forecasting models were developed by applying different structures regarding the number of the neurons in every hidden layer and the number of the hidden network layers. The final results have shown a very good forecasting accuracy of the predicted groundwater levels. This research can be very valuable in water resources and environmental management.

DETERMINATION OF THE RELATIVE MAGNETIC PERMEABILITY BY USING AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM AND 2D-FEM

Adaptive Neuro-fuzzy systems constitute an intelligent systems hybrid technique that combines fuzzy logic with neural networks in order to have better results. A study is presented to forecast the relative magnetic permeability using ANFIS. The global electromagnetic parameter, namely, the magnetic induction has been used as input to estimate the relative magnetic permeability. In this exceptional research, flnite element simulations are carried out to build up a database which will be used to train ANFIS network. The ANFIS approach learns the rules and membership functions from training data. The hybrid system is tested by the use of the validation data. Performance of the trained ANFIS network was compared with the multilayer feed forward network model and experimental results. The results show the efiectiveness of the proposed approach in solving inverse electromagnetic problem.