Solving transparency in drought forecasting using attention models

Droughts are one of the most devastating and recurring natural disaster due to a multitude of reasons. Among the dif-ferent drought studies, drought forecasting is one of the key aspects of effective drought management. The occurrence of droughts is related to a multitude of factors which is a combination of hydro-meteorological and climatic factors. These variables are non-linear in nature, and neural networks have been found to effectively forecast drought. How-ever, classical neural nets often succumb to over-fitting due to various lag components among the variables and there-fore, the emergence of new deep learning and explainable models can effectively solve this problem. The present study uses an Attention-based model to forecast meteorological droughts (Standard Precipitation Index) at short-term fore-cast range (1-3 months) for five sites situated in Eastern Australia. The main aim of the work is to interpret the model outcomes and examine how a deep neural network achieves the forecasting results. The plots show the importance of the variables along with its short-term and long-term dependencies at different lead times. The results indicate the im-portance of large-scale climatic indices at different sequence dependencies specific to the study site, thus providing an example of the necessity to build a spatio-temporal explainable AI model for drought forecasting. The use of such in-terpretable models would help the decision-makers and planners to use data-driven models as an effective measure to forecast droughts as they provide transparency and trust while using these models.

Automated summary

Droughts are devastating natural disasters, and forecasting them is essential for effective drought management. This study utilizes an Attention-based model to forecast short-term meteorological droughts in Eastern Australia and examines the functioning of deep neural networks in achieving accurate predictions.