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

• URL: http://arxiv.org/abs/2010.11787v1
• Date: Thu, 22 Oct 2020 15:01:22 GMT
• Title: Prediction of Rainfall in Rajasthan, India using Deep and Wide Neural Network
• Authors: Vikas Bajpai, Anukriti Bansal, Kshitiz Verma, Sanjay Agarwal
• Abstract summary: We propose a deep and wide rainfall prediction model (DWRPM) to predict rainfall in Indian state of Rajasthan. Information of geographical parameters (latitude and longitude) are included in a unique way. We compare our results with various deep-learning approaches like LSTM and CNN, which are observed to work well in sequence-based predictions.
• Score: 0.5735035463793008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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