Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter
This work addresses drought risk management by providing a method to appraise impacts using hydro-meteorological indicators, though it is incremental as it applies an existing method (XGBoost) to a specific domain with new data.
The paper tackled the problem of quantifying drought impacts by developing an XGBoost-based framework to predict multi-category impacts in Texas using the Standardized Precipitation Index (SPI) and Drought Impact Reporter (DIR) data, achieving outstanding performance in assessing impacts on agriculture, fire, society, plants, and relief.
Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. However, the relationships between the hydro-meteorological indicators and drought impacts are not identified well yet because of the complexity and data scarcity. In this paper, we proposed a framework based on the extreme gradient model (XGBoost) for Texas to predict multi-category drought impacts and connected a typical drought indicator, Standardized Precipitation Index (SPI), to the text-based impacts from the Drought Impact Reporter (DIR). The preliminary results of this study showed an outstanding performance of the well-trained models to assess drought impacts on agriculture, fire, society & public health, plants & wildlife, as well as relief, response & restrictions in Texas. It also provided a possibility to appraise drought impacts using hydro-meteorological indicators with the proposed framework in the United States, which could help drought risk management by giving additional information and improving the updating frequency of drought impacts. Our interpretation results using the Shapley additive explanation (SHAP) interpretability technique revealed that the rules guiding the predictions of XGBoost comply with domain expertise knowledge around the role that SPI indicators play around drought impacts.