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arXiv Machine Learning By Alessandro Grassi, Edoardo Kimani Bellotto, Wassim El Azami, Sabrina Outmani, Maximilien Houel

Neural-Network Maxent: a general extension with learned nonlinearity, applied to time-series for Desert Locust distribution modelling

Read the original on arXiv Machine Learning →

The paper introduces RNN Maxent, a new extension of the Maxent species distribution modelling framework that replaces its fixed linear feature dictionary with a neural network—specifically a Gated Recurrent Unit (GRU)—trained end‑to‑end via backpropagation. This approach retains Maxent’s presence‑only statistical foundations, background normalisation, and probability calibration while learning nonlinearity directly from data. Applied to Desert Locust distribution modelling using 50‑day environmental time‑series, RNN Maxent outperforms standard Maxent, achieving higher ROC AUC (0.862 vs. 0.792) and F1 scores (0.671 vs. 0.590).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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