[D] Geospatial ML for humanitarian drought/flood forecasting: critique my approach / ideas for predictive urgency index

I’m working on a non-commercial geospatial ML project (AidMap AI) focused on Central Asia/Afghanistan/Syria – predicting “urgency levels” for slow-onset ecological crises (droughts, floods, crop failure, hunger) using open data.

Core idea: aggregate multi-source data build a predictive model that outputs a composite “surgency score” (e.g., regression or multi-label classification) for anticipatory humanitarian action.

Current rough approach:

Data fusion: raster + tabular (e.g., point locations + time series)

Features: vegetation anomalies, precipitation deficits, population density, vulnerability indices

Model candidates: XGBoost/Random Forest for baseline, then spatiotemporal models or even lightweight transformers for time-series forecasting

Goal: near real-time-ish updates + forecasting horizon 1–3 months

Questions for feedback / discussion:

Best architectures for geospatial + temporal humanitarian forecasting? (how to handle irregular time series + sparse labels in conflict zones?)

Handling data bias / gaps in Global South regions (e.g., Afghanistan data quality, minority group underrepresentation)?

Low-resource / edge-friendly alternatives? (want to keep inference cheap for NGOs)

Existing open benchmarks/datasets for drought/flood prediction I might be missing? (beyond standard Kaggle ones)

Is this niche still valuable in 2026, or too redundant with WFP/Google/Atlas AI tools?