Estimation of Evaporation Using Long Short Term Memory and Gated Recurrent Unit Based Neural Network
Vishal I. Mehra
*
College of Agricultural Information Technology, Anand Agricultural University, Anand, Gujarat, India.
Arvind N. Nakiya
College of Food Processing Technology and Bio-Energy, Anand Agricultural University, Anand, Gujarat, India.
J. Sravan Kumar
College of Agricultural Engineering and Technology, Anand Agricultural University, Anand, Gujarat, India.
*Author to whom correspondence should be addressed.
Abstract
This study presents the comparison of conventional Artificial Neural Network (ANN) and advanced neural networks to predict weekly potential evaporation for Anand, Gujarat, India, which comes under the subtropical climatic zone. Recently, many advanced deep neural structures like Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) have been introduced that have excellent prediction accuracy. Climate data such as bright sunshine hours, rainfall, wind speed, maximum and minimum temperature, and maximum and minimum relative humidity have been used to train and test the conventional and advanced neural network models. A comparison was made for the estimation of evaporation predicted by these models. The performance results show that deep neural network models with advanced structures like LSTM and GRU have performed better in terms of Root Mean Square Error and correlation coefficient and are able to learn the events very well in comparison to conventional neural network structures.
Keywords: Evaporation, deep neural network, long short term memory, gated recurrent unit