Rainfall prediction using ensembled-LSTM and dense networks. (3rd February 2023)

• Record Type: Journal Article
• Authors: Sinha, Ujjwal; Thakur, Vishal; Jain, Sammed; Parimala, M.; Kaspar, S.
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 14:Number 2(2023)
• Page Start: 59
• Page End: 70
• Publication Date: 2023-02-03
• Subjects: rainfall prediction -- long short-term memory -- LSTM -- forecasting -- weather -- Root mean squared error -- RMSE -- precipitation -- humidity -- wind speed -- time series; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Rainfall prediction has been of utmost importance in any country. The amount of rainfall in a particular region has been known to affect the growth in that area, especially in an agriculture-based country like India. This paper proposes a model which performs one step rainfall forecasting in the regions Ranakpur and North-Eastern states of Assam and Meghalaya based on time series data acquired from 1 and 75 weather stations in both areas, respectively. This model was chosen to be based on the LSTM algorithm which has proven to be better than existing rainfall prediction models based on linear regression, support vector regressors, artificial neural network, random forest and decision tree algorithms. The RMSE score of the proposed architecture for Ranakpur and North-East were 1.948 and 2.654 respectively, better than the algorithms used in comparison. The factors taken into consideration for while predicting the weather are max temperature, min temperature, precipitation, wind speed, relative humidity and solar radiation.