| Author(s) | Saud A. Taher |
| Affiliation | Civil Engineering Department College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia |
| Title | Estimation of Potential Evaporation: Artificial Neural Networks Versus Conventional Methods |
| Source | Journal of King Saud University. Engineering Sciences. Volume 17, No 1. (2005/1425) |
| Abstract | Accurate estimation of potential evaporation, especially in arid regions such as Saudi Arabia, has been of a great concern to many researchers. Its importance is obvious in many water resources applications such as management of hydrologic, hydraulic and agricultural systems. For this purpose, four three-layer backpropagation neural networks were developed to forecast monthly potential evaporation in Riyadh, Saudi Arabia, based on four explanatory climatic factors. Observations of relative humidity, solar radiation, temperature, wind speed and evaporation for the past 22 years have been used to train and test the developed networks. Results revealed that the networks were able to well learn the events they were trained to recognize. Moreover, they were capable of effectively generalizing their training by predicting evaporation for sets of unseen cases. These encouraging results were supported by high values of coefficient of correlation and low mean square errors reaching 0.98 and 0.00015 respectively. The study has also evolved a comparison with traditional methods and has proven that the developed neural networks were superior. Keywords: Potential evaporation, Climatic factors, Artificial neural networks, Saudi Arabia |
