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arXiv Machine Learning By Fangjian Zhang, Xiaoyong Zhuge, Wenlan Wang, Haixia Xiao, Yuying Zhu, Siyang Cheng, Ali Mamtimin

Hurdle-RMIL: Addressing Zero Inflation and Long-Tailed Imbalance in Infrared Rainfall Retrieval

Read the original on arXiv Machine Learning →

The paper introduces Hurdle‑RMIL, a two‑stage model that first separates zero‑inflated rainfall from the long‑tailed distribution of positive rainfall and then applies a Bayes‑based transformation to learn a balanced‑distribution model from natural data. Experiments over multiple Chinese regions show that Hurdle‑RMIL reduces systematic underestimation of rare high‑intensity rainfall, improves detection of extreme events, and achieves higher equitable threat scores without significantly harming lower‑threshold accuracy.

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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