Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter

• URL: http://arxiv.org/abs/2211.02768v1
• Date: Fri, 4 Nov 2022 22:16:13 GMT
• Title: Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter
• Authors: Beichen Zhang (1), Fatima K. Abu Salem (2), Michael J. Hayes (1), Tsegaye Tadesse (1) ((1) School Of Natural Resources, University of Nebraska-Lincoln, (2) Computer Science Department, American University of Beirut)
• Abstract summary: Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. We propose a framework based on the extreme gradient model (XGBoost) for Texas to predict multi-category drought impacts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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