Title: Precipitation nowcasting using ensemble learning approaches

Authors: Nita H. Shah; Bipasha Paul Shukla; Anupam Priamvada

Addresses: Department of Mathematics, Gujarat University, Ahmedabad-380009, India ' Atmospheric Sciences Division, Space Applications Centre, ISRO, Ahmedabad-380015, Gujarat, India ' Department of Mathematics, Gujarat University, Ahmedabad-380009, India

Abstract: The data from the automatic weather stations (AWS) though extremely important is yet to be fully explored from the perspective of weather forecasting. The proposed article experimented with a different setup of each ensemble learning technique XGBoost, AdaBoost and random forest with different oversampling techniques. The experiments lead us to develop an algorithm that is a linear combination of multilinear regression, XGBoost, AdaBoost and random forest. The predictors consist of time series of in-situ observations. We have also studied the impact of in-situ observations on the rainfall for the next few hours based on misclassification error. The results indicate that the most influential feature extracted from the proposed algorithm is humidity and rainfall while other meteorological variables are found to be weak predictors. The average accuracy of the proposed algorithm is 87%.

Keywords: ensemble learning; XGBoost; AdaBoost; random forest; oversampling; misclassification error.

DOI: 10.1504/IJGW.2022.127068

International Journal of Global Warming, 2022 Vol.28 No.4, pp.387 - 399

Received: 20 Apr 2022
Received in revised form: 13 Jun 2022
Accepted: 13 Jun 2022

Published online: 21 Nov 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article