ESTIMATION OF REFERENCE EVAPOTRANSPIRATION USING ARTIFICIAL NEURAL NETWORK FOR MOHANPUR, NADIA DISTRICT, WEST BENGAL: A CASE STUDY

Alivia Chowdhury, Debaditya Gupta, Dhananjay Paswan Das, Anirban Bhowmick

Abstract: Estimation of evapotranspiration plays a key role in various water management studies including irrigation scheduling and water budgeting. Being an extremely complex and non-linear phenomena, precise estimation of evapotranspiration requires large number of climatological data as well as vast time. In recent past, artificial neural network has emerged as a successful tool to model complex non-linear relationships including evapotranspiration process. The current study investigates the potential of artificial neural network models to estimate reference evapotranspiration (ET0) for Mohanpur area and compares the performance of ANN models with reference ET estimated by FAO-Penman method. Different combinations of six weather parameters namely maximum air temperature, minimum air temperature, maximum relative humidity, minimum relative humidity, wind velocity and actual sunshine hours were used as inputs to train the 12 multilayer feed forward perceptron ANN models selected for the study. The FAO-56 Penman estimated ET0 was used as output for all the models. The models were trained with back propagation learning algorithm. The analysis is carried out in MATLAB software. For each combination of input parameters, the best ANN model was selected with least SEE and highest R2 . The result of the study inferred that ANN performed very well with all the input parameters which were used in reference ET estimation by FAO-Penman method but the ANN models with less input variables also yielded very good estimation of ET0. Therefore, it can be suggested that ANN method can be used for ET0 estimation for the study area with high degree of accuracy in limited data condition also

Keywords: Evapotranspiration, artificial neural network, Penman-Monteith method, Mohanpur

DOI: https://doi.org/10.15623/ijret.2017.0607021