  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.
