Generalized wavelet neural networks for evapotranspiration modeling in India

Abstract

This study focuses on the application of generalized wavelet neural network (GWNN) models corresponding to the FAO-56 Penman Monteith (FAO-56 PM), Turc, and Hargreaves (HG) methods for estimating daily reference evapotranspiration (ET_o). The daily pooled climate data from 15 different locations under 4 different agro-ecological regions (AERs: semi-arid, arid, sub-humid, and humid) in India are used as an input to GWNN models. The inputs include three combinations of climate data (minimum and maximum air temperatures, minimum and maximum relative humidity, wind speed, and solar radiation) and the target consists of the FAO-56 PM estimated ET_o. Further, the GWNN models were applied to 15 individual model development locations and 10 different model testing locations to test the generalizing capability. Comparison of developed GWNN models was made with the classic generalized artificial neural network (GANN), generalized linear regression (GLR), generalized wavelet regression (GWR), and corresponding conventional methods to test the superiority of one model over the other. Results reveal that the GWNN models followed by GANN models performed better than GWR and GLR models for four AERs. The testing results suggest that the GWNN and GANN models have better generalizing capabilities than the GWR and GLR for almost all region locations.

Keywords:

• Neural networks
• discrete wavelet transformation
• evapotranspiration
• agro-ecological regions

Acknowledgments

The authors wish to thank All India Coordinated Research Project on Agrometeorology (AICRPAM), Central Research Institute for Dryland Agriculture (CRIDA), Hyderabad, Andhra Pradesh, India for providing the requisite climate data for carrying out this study. Also, the authors express their gratitude to the reviewers for useful comments and suggestions.