Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions often relies on sensor data that may not be accessible for a new, previously unmonitored location. In response to these challenges, we propose a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations.

Enhanced Dengue Outbreak Prediction in Tamilnadu using Meteorological and Entomological data Dr. Varalakshmi M, VIT Vellore, India, Dr. Daphne Lopez, VIT Vellore, India Sponsored by: ISRO Acknowledgement: Dr. VinothKumar S, DD/CHO, Madurai Corporation, TamilNadu Public Health Department, India Abstract This paper focuses on studying the impact of climate data and vector larval indices on dengue outbreak. After a comparative study of the various LSTM models, Bidirectional Stacked LSTM network is selected to analyze the time series climate data and health data collected for the state of Tamil Nadu (India), for the period 2014 to 2020. Prediction accuracy of the model is significantly improved by including the mosquito larval index, an indication of VBD control measure. Introduction Dengue Fever (DF), an outbreak prone viral infection is transmitted by Aedes mosquitoes, which is mostly found in tropical and sub-tropical climatic regions. The infection can result in Dengue Haemorrhagic Fever (DHF), also known as severe dengue which can be fatal.