Interpretable Multimodal Machine Learning for Predicting and Explaining Livelihood Vulnerability to Drought in East Africa

Suggested By : Leizel De la Cruz, Lorenz Wendt

Objective:
To develop an interpretable machine learning framework for assessing livelihood vulnerability to drought in the arid regions of East Africa by integrating multimodal data (including Earth Observation, socio-economic and ancillary data) to enhance vulnerability prediction and identify the most influential underlying factors using SHapley Additive exPlanations (SHAP), thereby providing actionable insights for enhanced early warning systems.

Short Description:
Assessing livelihood vulnerability to drought is critical for proactive resilience-building in arid and semi-arid areas in East Africa. The traditional index-based methods usually depend on linear assumptions and expert-weighted indicators that might have overlooked the complex and non-linear relationships among factors contributing to vulnerability. This study proposes a data-driven approach that uses machine learning (ML) for prediction and the SHapley Additive exPlanations (SHAP) framework to explain and interpret the model output and identify the most influential predictors.

This research will first integrate different datasets including satellite drought indices; household survey data and other information related to livelihoods to develop a comprehensive dataset of predictors. Different ML models (like regression, XGBoost, random forest) will be trained on the historical vulnerability of livelihoods in district-level and identify the best model performance. The novelty of this research is in the application of SHAP analysis for an attempt to move beyond the "black box" nature of ML, as this approach quantifies the contribution of each identified factors (e.g. rainfall anomaly, SPEI, NDVI, market price fluctuation, distance to market) to the model’s ability to predict vulnerability. By making complex model outputs transparent and actionable, this research may provide a strong decision-support tool for enhancing drought resilience and decrease impacts to livelihoods in highly climate-sensitive regions.

Start: Anytime

Relevant Studies:

1. Crausbay, Shelley D., Kimberly R. Hall, Molly S. Cross, Meghan Halabisky, Imtiaz Rangwala, Jesse Anderson, and Ann Schwend. 2024. “A Flexible Data-Driven Approach to Co-Producing Drought Vulnerability Assessments.” Ecosphere 15(10): e70040. https://doi.org/10.1002/ecs2.70040
2. Enenkel, M., Steiner, C., Mistelbauer, T., Dorigo, W., Wagner, W., See, L., Atzberger, C., Schneider, S., & Rogenhofer, E. (2016). A Combined Satellite-Derived Drought Indicator to Support Humanitarian Aid Organizations. Remote Sensing, 8(4), 340. https://doi.org/10.3390/rs8040340
3. IPCC, 2022: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., doi:10.1017/9781009325844.
4. Lu, R., Liu, S., Duan, H., Kang, W., & Zhi, Y. (2024). Combining the SHAP Method and Machine Learning Algorithm for Desert Type Extraction and Change Analysis on the Qinghai–Tibetan Plateau. Remote Sensing, 16(23), 4414. https://doi.org/10.3390/rs16234414