Neural computing: an introduction
Neural computing: an introduction
Neural networks for pattern recognition
Neural networks for pattern recognition
A Simple Neural Network Pruning Algorithm with Application to Filter Synthesis
Neural Processing Letters
An Algorithm for Automatic Design of Two Hidden Layered Artificial Neural Networks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
A nonlinear rainfall-runoff model using neural network technique: example in fractured porous media
Mathematical and Computer Modelling: An International Journal
Design of neural networks for fast convergence and accuracy: dynamics and control
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The ability of artificial neural networks (ANN) to model the rainfall-discharge relationships of karstic aquifers has been studied in the Terminio massif (Southern Italy), which supplies the Naples area with a yearly mean discharge of approximately 1-3.5m^3/s. The Mediterranean climate causes a rapid increase in evapotranspiration and a decrease in rainfall towards spring-summer. Especially during drought, and in combination with highly sensitive climatic parameters, there are dramatic changes in the discharge amount especially during the July and August months. A neural network model was developed based on MLP (multi-layer perceptron) network to forecast of water resources three and six month before the main stress months of July and August. Example data were extracted on an ultra-centenarian hydrological serial. The training and validation phases, confirmed by a ten fold cross validation methodology, led to a very satisfactory calibration of the ANN model, with errors in forecasting discharge values of just 5% (three months before) and 10% (six months before).