Feasibility assessment of machine learning for predicting heatwaves in Bangladesh

Abstract

As extreme weather phenomena, heatwaves bring severe risks to human health, society, and ecosystems. Over the past few years, Bangladesh has experienced heatwaves that are becoming more frequent and intense. Early warning systems (EWS) can help to minimize the potential damage from these events by providing sufficient time for thorough and effective preparation. Traditionally, numerical weather prediction (NWP) is employed for heatwave forecasting, but it is both expensive and time-consuming. This study explores the potential of using machine learning as a faster and more cost-effective alternative to NWP. Specifically, we focus on building an artificial neural network (ANN) to predict heatwaves three days in advance over Bangladesh. Our model utilizes 28 features to predict a binary target value (0 for no heatwave, 1 for heatwave). The results are promising, with the model achieving an accuracy of 91% in distinguishing heatwave and non-heatwave days. This suggests that machine learning can be a valuable tool for large-scale heatwave prediction in Bangladesh