Forecasting Rainfall with Recurrent Neural Network for irrigation equipment

Abstract

The irrigation decision-making system based on Knowledge-based Engineering (KBE) can accurately predict water requirements and realize smart irrigation. Recurrent neural network(RNN) model have recently showed state-of-the-art performance in this system. This paper deals with the problem of long-term rainfall forecasting based on this network which predicts target rainfalls based on contextual information. A novel recurrent neural network with long short term memory(LSTM) is put for model sequence process for forecasting rainfall. Back-propagation through time(BPTT) algorithm is described for updating recurrent network's weights. Extensive empirical comparison with three networks, Feed-forward neural network (FNN), Wavelet neural network(WNN) and Auto-regressive Integrated Moving Average(ARIMA), are also provided at various numbers of parameters and configurations. Simulation results demonstrate that the recurrent model with LSTM, trained by the suggested methods, outperforms the others networks.