Multilayer perceptron neural network based models for prediction of the rainfall and reference crop evapotranspiration for sub-humid climate of Dapoli, Ratnagiri District, India

Abstract

Rainfall and reference crop evapotranspiration (ETo) are two important climatic parameters pertaining to agriculture production. Rainfall displays variability whereas reference crop evapotranspiration is nonlinear dynamic and complex phenomena. Reference crop evapotranspiration is an important component in calculation of crop water requirements, climatological and hydrological studies. In the present study, eight independent models are developed by using widely employed artificial intelligence (AI) formalism, namely, multilayer perceptron-artificial neural network (MLP-ANN). The proposed models predict two important weather parameters, viz. rainfall and reference crop evapotranspiration (ETo) using meteorological data of 34 calendar years (1983–2016) at Dapoli, District Ratnagiri, Maharashtra, India. The input space of the models consists of six climatic variables, namely, maximum temperature (Tmax), minimum temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), wind speed (WS), and sunshine duration (SSD). The high correlation coefficient (CC) values (0.900 to 0.957), and low root mean square error (RMSE) values (0.543 to 22.057) for both training along with test data set indicate very good prediction accuracy and generalization performance of the proposed models. Significant features of this study are (i) it is first instance where in MLP-ANN based models are developed for the rainfall and ETo prediction for meteorological data at Dapoli, Dist. Ratnagiri, Maharashtra, India. (ii) MLP-ANN modeling formalism is found to be an efficient tool for ETo prediction, and the developed models can be gainfully utilized for the further prediction of similar weather parameters in future.