A Data Stream Approach to Predicting Risk: An Incremental Learning Model

Abstract

Risk prediction in conflict-affected environments requires models that can learn from evolving data streams. This study develops an incremental recurrent learning framework for adverse-event count prediction and evaluates it in a field study of human-social-infrastructure projects in Afghanistan. The framework implements GRU-Linear (GRU-L) and LSTM-Linear (LSTM-L) predictors that encode short historical sequences with a recurrent layer and map the learned representation to nonnegative count predictions through a fully connected regression head. For each arriving instance, the model first predicts the target and then updates its parameters after the true value becomes available, following a prequential (test-then-train) evaluation protocol. We compare these predictors with statistical, ensemble, gradient-boosted, recurrent batch, and rule-based stream baselines under a unified chronological protocol. Batch models use expanding-window refitting, whereas stream models use prequential test-then-train evaluation, ensuring identical information availability constraints across all model classes. The experiments report MAE, RMSE, paired 400-instance block-level significance tests, sliding-window error trajectories, Page-Hinkley prediction-error drift alarms, and runtime. The results show that LSTM-L obtains the lowest MAE for Events and Wounded, and GRU-L obtains the lowest MAE for Hijacked and Dead. Linear Regression (LR) and TargetMean provide the lowest runtimes, while the recurrent stream models offer stronger MAE performance, particularly when recent temporal patterns are predictive. The study demonstrates that incremental recurrent learning can support adaptive risk monitoring when adverse-event distributions change over time.