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

arXiv: https://arxiv.org/abs/2010.11787 · HTML: https://mlsanity.com/paper/2010.11787/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [A Deep and Wide Neural Network-based Model for Rajasthan Summer Monsoon Rainfall (RSMR) Prediction](https://mlsanity.com/paper/2103.02157/) (2021)

## Abstract (arXiv)

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.