| Popis: |
Precise estimation of potential evaporation, has a great significance in many water resources applications such as management of hydrologic, hydraulic and agricultural systems. Although there are empirical formulas available for Evaporation estimation, but their performances are not all satisfactory due to the complex nature of the evaporation process and the data availability. For this purpose, artificial neural networks (ANN) models was developed to estimate monthly potential evaporation in Pantagar, US Nagar (India) based on four instructive climatic factors. Observations of relative humidity, solar radiation, temperature, wind speed and evaporation for the past 19 years and 8 months (total 236 months) have been used to train and test the developed models. Results shown that the model was able to well learn the events they were trained to recognize. These encouraging results were supported by high values of coefficient of correlation and low mean square errors. The correlation coefficient was found 0.9236 and root mean square error was 0.9863 for testing data sets. |
