Next Generation Drought Monitoring: Forecasting to Emotion-Focused Coping

A severe drought causes catastrophic economic losses, resulting in mental degradation/deterioration. While drought monitoring has been focused on detecting and characterizing an emerging drought (physical system-focused), artificial intelligence with big data from social monitoring provides a unique opportunity to investigate sentimental alterations of the public along the drought propagations and explore their triggers (social system-focused). This study examines the potential of an AI technique, Natural Language Processing (NLP), in monitoring sentimental alterations of the public. This study is a case study of the recent Korea drought, leveraging X (formerly, twitter) and Google Trends data. In this study, we evaluate the seasonal-to-seasonal predictability of drought measures in the southwest region of the Korean Peninsula for the 2022/23 period and analyze spatiotemporal changes in media and public interest in drought phenomena during the 2022/23 drought period through newspapers and social media. Initially, to understand the predictability of drought measures in March 2023, we evaluate the predictability of drought measures based on probabilistic and deterministic seasonal-to-seasonal forecasts for the 2022/23 Korea drought. Subsequently, using drought-related articles in newspapers and Twitter data from the 2022 to 2023, we utilize natural language processing and text mining technologies to detect and monitor the topic and emotional alternation of the titles of news article and public, respectively, regarding the 2022/23 drought. The results of this study indicate that the predictability of drought measures in May 2023 is skillful at the sub-seasonal scale but limited at the seasonal scale. Statistical forecasts provide crucial information on precipitation needed for drought recovery through weekly precipitation forecasts, aiding in assessing the drought condition. The public interest in the 2022/23 drought shows spatiotemporal differences based on the drought-affected areas and drought stages. Especially in April 2023, when a severe drought occurred in the southwestern Korean region, an increase in the number of newspaper articles with negative titles was observed, and negative emotions were detected from the social media data. This study provides an insight about the role of AI in developing the next-generation drought monitoring system.