Data-Efficient LSTM Modeling for Climate-based Dengue Early Warning in Lampung, Indonesia - Rifky Fauzi; Mia Syntia Br Sinaga; Nela Rizka; Dear Michiko Mutiara Noor; Aswan Anggun Pribadi; Tiara Shofi Edriani (UIN Sumatera Utara, 2025)
We present a data-efficient recurrent framework for climate-informed dengue early warning in Lampung Province. Monthly incidence and climate records are transformed into supervised sequences with 2-3-month lags, consistent with the observed lead-lag structure. Three architectures i.e. single-layer LSTM, stacked LSTM, and Temporal-Attention LSTM (TA-LSTM) are tuned via a compact genetic search under a time-ordered split. Performance improves with longer history; the TA-LSTM (37 units) attains the best accuracy. Permutation feature importance reveals a clear hierarchy: relative humidity and maximum temperature dominate, autoregressive incidence contributes moderately, while rainfall, sunshine, and minimum temperature are secondary; average temperature is largely redundant. The findings indicate that adding meaningful historical context and selective temporal weighting yields robust early-warning capability from coarse, time-limited data, and that humidity-temperature dynamics, together with short-term incidence persistence, are the principal drivers in this provincial setting.