Science Alert
Curve Top
Journal of Applied Sciences
  Year: 2009 | Volume: 9 | Issue: 9 | Page No.: 1786-1790
DOI: 10.3923/jas.2009.1786.1790
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Forecasting Precipitation with Artificial Neural Networks (Case Study: Tehran)

M.H. Gholizadeh and M. Darand

Artificial Neural Networks (ANN), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Rainfall forecasting has been a difficult subject due to the complexity of the physical processes involved and the variability of rainfall in space and time. Artificial Neural Networks (ANN), which perform a nonlinear mapping between inputs and outputs, are one of the techniques that are suitable for rainfall forecasting. Multiple perceptron neural networks were trained with actual monthly precipitation data from Tehran station for a time period of 53 years. Predicted amounts are of next-month-precipitation in the next year. The ANN models provided a good fit with the actual data and have shown a high feasibility in prediction of month rainfall precipitation. Combination neural networks with Genetic algorithm showed better results.
PDF Fulltext XML References Citation Report Citation
  •    Prediction of Monthly Rainfall for Selected Meteorological Stations in Iraq using Back Propagation Algorithms
  •    A Multi Layer Perceptron Neural Network Trained by Invasive Weed Optimization for Potato Color Image Segmentation
  •    Codebook Enhancement in Vector Quantization Image Compression using Backpropagation Neural Network
  •    Application of ANN and ANFIS Models on Dryland Precipitation Prediction (Case Study: Yazd in Central Iran)
  •    Comparison of Neural Network and K-Nearest Neighbor Methods in Daily Flow Forecasting
How to cite this article:

M.H. Gholizadeh and M. Darand, 2009. Forecasting Precipitation with Artificial Neural Networks (Case Study: Tehran). Journal of Applied Sciences, 9: 1786-1790.

DOI: 10.3923/jas.2009.1786.1790






Curve Bottom