Credit risk evaluation or loan default risk evaluation is important to financial institutions which provide loans to businesses and individuals. Credit and loans have risk of being defaulted. To understand risk levels of credit users (corporations and individuals), credit providers (bankers)...

One of the most significant factors influencing the liquidity of the financial market is the amount of currency in circulation. Although the central bank is responsible for the distribution of the currency it cannot assess the demand for the currency, as that demand is influenced by the...

We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural...

Artificial Neural Network Model for prediction of time-series data is revisited on analysis of the Indonesian stock-exchange data. We introduce the use of Multi-Layer Perceptron to percept the modified Poincare map of the given financial time-series data. The modified Poincare map is believed to...

A nonlinear ensemble prediction model for typhoon rainstorm has been developed based on particle swarm optimization-neural network (PSO-NN). In this model, PSO algorithm is employed for optimizing the network structure and initial weight of the NN with creating multiple ensemble members. The...

Potential evapotranspiration (ET<Subscript>o</Subscript>) is an essential hydrologic parameter for having better understanding for hydrologic cycle in certain catchment area. In addition, ET<Subscript>o</Subscript> is vital for calculating the agricultural demand. In fact, Penman-Monteith (PM) method is considered as reference method for...</subscript></subscript>

The paper presents models for better environmental intelligent management within water supply systems. The following computer models were developed: supervising parameters (pressure and flow) of water supply network (classification models in the form of neural networks, hybrid neural networks,...

This paper explored the potential of Backpropagation Neural Network (BNN) and M5 model tree based regression approach to estimate the mean annual flood. Data used in this study were taken from an earlier study by Swamee et al. (J Water Resour Plan Manage 121:403–407, <CitationRef CitationID="CR23">1995</CitationRef>) for 93 Indian...</citationref>

A Manhattan search algorithm to minimize artificial neural network error function is outlined in this paper. From an existing position in Cartesian coordinate, a search vector moves in orthogonal directions to locate minimum function value. The search algorithm computes optimized step length for...

Sediment load estimation is essential in many water resources projects. In this study, the capability of two different types of model including SWAT as a process-based model and ANNs as a data-driven model in simulating sediment load were evaluated. The issue of uncertainty in the simulated...