Explainable AI in drought forecasting

Machine Learning with Applications

Abhirup Dikshit¹  Biswajeet Pradhan¹ 

[1] Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, NSW 2007, Australia;

关键词: Drought forecasting;    Standard precipitation index;    Deep learning;    Explainable AI;   

DOI  :  

来源: DOAJ

【 摘 要 】

Droughts are one of the disastrous natural hazards which has severe impacts on agricultural production, economy, and society. One of the critical steps for effective drought management is developing a robust forecasting model and understanding how the variables affect the model outcomes. The present study forecasts SPI-12 at a lead time of 3 months, using the Long Short-Term Memory (LSTM) model, and further interprets the spatial and temporal relationship between variables and forecasting results using SHapley Additive exPlanations (SHAP). The developed model is tested in four different regions in New South Wales (NSW), Australia. SPI-12 was computed using monthly rainfall data collected from Scientific Information for Land Owners (SILO) for 1901–2018. The model was trained from 1901–2000 and tested from 2001–2018, and the performance was measured using Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (NSE) and Root-Mean-Square-Error (RMSE). To understand the underlying impact of variables on the model outcomes, SHAPley values were calculated for the entire testing period and also at three different temporal ranges, which are during the Millennium Drought (2001–2010), post drought period (2011–2018) and at a seasonal scale (summer months). The comparison of the results shows a significant variation in the impact of variables on forecasting, both temporally and spatially. It also shows the need to study the model outcomes for specific regions and for a shorter duration than the entire testing period. This is a first of its study towards interpreting the forecasting model in drought studies, which could help understand the behaviour of drought variables.

【 授权许可】

Unknown