Berlin 2024 – wissenschaftliches Programm
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DY: Fachverband Dynamik und Statistische Physik
DY 49: Focus Session: Computing with Dynamical Systems: New Perspectives on Reservoirs and Applications II – Applications and Quantum RC
DY 49.6: Vortrag
Donnerstag, 21. März 2024, 16:30–16:45, BH-N 243
Forecasting Food Security with Reservoir Computing — •Joschka Herteux1,2, Christoph Räth1, Amine Baha3, Giulia Martini2, and Duccio Piovani2 — 1Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) — 2World Food Programme, Research, Assessment and Monitoring Division (RAM) — 3World Food Programme Innovation Accelerator
Early warning systems are an essential tool for effective humanitarian action. Advance warnings on impending disasters facilitate timely and targeted response which help save lives, livelihoods, and scarce financial resources. We present a quantitative methodology based on Reservoir Computing (RC) to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme’s integrated global hunger monitoring system (https://hungermap.wfp.org/). We compare the performance of the RC model to various algorithms including ARIMA, XGBoost, LSTMs and CNNs spanning from classical statistical to deep learning approaches. Our findings highlight Reservoir Computing as a particularly well-suited model for this task given both its notable resistance to over-fitting on limited data samples and its efficient training capabilities. This work constitutes a successful application of RC on high-dimensional, heterogenous, real data and has been submitted to Nature Communications.
Keywords: Reservoir Computing; Machine Learning; Food Security; Application; Time Series Forecasting