Prediction of Rainfall in Rajasthan, India using Deep and Wide Neural Network
Rainfall is a natural process which is of utmost importance in various areas including water cycle, ground water recharging, disaster management and economic cycle. Accurate prediction of rainfall intensity is a challenging task and its exact prediction helps in every aspect. In this paper, we propose a deep and wide rainfall prediction model (DWRPM) and evaluate its effectiveness to predict rainfall in Indian state of Rajasthan using historical time series data. For wide network, instead of using rainfall intensity values directly, we are using features obtained after applying a convolutional layer. For deep part, a multi layer perceptron (MLP) is used. Information of geographical parameters (latitude and longitude) are included in a unique way. It gives the model a generalization ability, which helps a single model to make rainfall predictions in different geographical conditions. We compare our results with various deep learning approaches like MLP, LSTM and CNN, which are observed to work well in sequence based predictions. Experimental analysis and comparison shows the applicability of our proposed method for rainfall prediction in Rajasthan.
ArXiv