A Deep and Wide Neural Network-based Model for Rajasthan Summer Monsoon Rainfall (RSMR) Prediction

TL;DR

This paper evaluates deep learning models, including CNN, MLP, and Wide Deep Neural Networks, for predicting Rajasthan summer monsoon rainfall, finding the Wide Deep Neural Network to be most effective.

Contribution

It introduces a comparative analysis of deep learning approaches for monsoon rainfall prediction using two distinct datasets, highlighting the superior performance of the Wide Deep Neural Network.

Findings

Wide Deep Neural Network outperforms other models

Deep learning models effectively predict monsoon rainfall

Analysis on two datasets validates model robustness

Abstract

Importance of monsoon rainfall cannot be ignored as it affects round the year activities ranging from agriculture to industrial. Accurate rainfall estimation and prediction is very helpful in decision making in the sectors of water resource management and agriculture. Due to dynamic nature of monsoon rainfall, it's accurate prediction becomes very challenging task. In this paper, we analyze and evaluate various deep learning approaches such as one dimensional Convolutional Neutral Network, Multi-layer Perceptron and Wide Deep Neural Networks for the prediction of summer monsoon rainfall in Indian state of Rajasthan.For our analysis purpose we have used two different types of datasets for our experiments. From IMD grided dataset, rainfall data of 484 coordinates are selected which lies within the geographical boundaries of Rajasthan. We have also collected rainfall data of 158 rain gauge station from water resources department. The comparison of various algorithms on both these data sets is presented in this paper and it is found that Deep Wide Neural Network based model outperforms the other two approaches.