Evaporation Modelling by using Artificial Neural Network and Multiple Linear Regression Technique

Abstract

In this study an artificial neural network based evaporation estimation model was developed and evaluated the performance of the developed model for Udaipur, Rajasthan, India. Multiple linear regressions were used to estimate the pan evaporation for the study area and to model the linear correlations between a single dependent variable Y and two or more independent variables. Performance of the models was evaluated by using qualitative and quantitative indices, viz. correlation coefficient (CC) and root mean square error (RMSE). The values of root mean square error were 0.836 and 0.882 and the values of correlation coefficient were 0.970 and 0.960 for network 4-6-6-1 for training and testing period respectively. The values of root mean square error were 1.028 and 1.106 and the values of correlation coefficient were 0.941 and 0.930 for MLR model for training and testing period respectively. In this study, it was found that the evaporation estimation done through ANN was better than compared to that estimation through MLR.

Publication
In International Journal of Agricultural and Food Science, 5(4), pp. 125-133
Avatar
Javed Ali
Doctoral Researcher

Related