Abstract
Rainfall prediction is an important task due to the dependence of many people on it, especially in the agriculture sector. Prediction is difficult and even more complex due to the dynamic nature of rainfalls. In this study, we carry out monthly rainfall prediction over Simtokha a region in the capital of Bhutan, Thimphu. The rainfall data were obtained from the National Center of Hydrology and Meteorology Department (NCHM) of Bhutan. We study the predictive capability with Linear Regression, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Long Short Term Memory (BLSTM) based on the parameters recorded by the automatic weather station in the region. Furthermore, this paper proposes a BLSTM-GRU based model which outperforms the existing machine and deep learning models. From the six different existing models under study, LSTM recorded the best Mean Square Error (MSE) score of 0.0128. The proposed BLSTM-GRU model outperformed LSTM by 41.1% with a MSE score of 0.0075. Experimental results are encouraging and suggest that the proposed model can achieve lower MSE in rainfall prediction systems.
Highlights
Rainfall prediction has a widespread impact ranging from farmers in agriculture sectors to tourists planning their vacation
The study of deep learning methods for rainfall prediction is presented in this paper, and a Bidirectional Long Short Term Memory (BLSTM)-Gated Recurrent Unit (GRU) based model is proposed for rainfall prediction over the Simtokha region in Thimphu, Bhutan
The traditional Multi-Layer Perceptron (MLP), which is widely used for rainfall prediction, did not perform well in comparison to the recent deep learning models on weather station data
Summary
Rainfall prediction has a widespread impact ranging from farmers in agriculture sectors to tourists planning their vacation. The second approach is using pattern recognition These algorithms are decision tree, k-nearest neighbor, and rule-based methods. Deep learning techniques are used to find meaningful results, and these techniques are based on the neural network. In this method, we ignore the physical laws governing the rainfall process and predict rainfall patterns based on their features. This study aims to use pattern recognition to predict precipitation. The predictive models developed in this study are based on deep learning techniques. We propose a Bidirectional Long Short Term Memory (BLSTM) and Gated Recurrent Unit (GRU)-based approach for monthly prediction and compare its results with the state-of-the-art models in deep learning
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