Prediction of Rainfall in Rajasthan, India using Deep and Wide Neural Network

TL;DR

This paper introduces a novel deep and wide neural network model for rainfall prediction in Rajasthan, combining convolutional features with a multi-layer perceptron and geographical data for improved accuracy.

Contribution

The paper proposes a new deep and wide neural network model that integrates convolutional features and geographical parameters for rainfall prediction in Rajasthan.

Findings

The proposed model outperforms traditional deep learning methods like MLP, LSTM, and CNN.

Inclusion of geographical parameters enhances model generalization across different regions.

Experimental results confirm the effectiveness of the deep and wide approach for rainfall prediction.

Abstract

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.