arXiv Machine Learning July 28, 2026 By Qingyi Yang, Siqian Qiu, Bing Li, Xu Shan, Jia Feng, Shunan Zhou, Xudong Zhou, Tiantian Xing, Jiale Guo, Xiaoyi Dong, Gaoyu Liu, Xiaohuan Liu, Haiqing Pu, Qingwen Deng, Xun Zhang, Zhongrun Xiang, Haiyang Qian, Ying Yan, Yongkang Xu, Nuo Lei, Tianlong Jia, Baoying Shan, Carlo De Michele

HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows

Read the original on arXiv Machine Learning →

arXiv:2607. 23983v1 Announce Type: cross Abstract: Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer.

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.

arXiv AI

Jul 28

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.

By Maria Luisa Taccari, Kenza Tazi, Ois\'in M. Morrison, Andreas Grafberger, Juan Colonese, Corentin Carton de Wiart, Christel Prudhomme, Cinzia Mazzetti, Matthew Chantry, Florian Pappenberger

fine-tuning benchmarks

arXiv Machine Learning

Sep 1

A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

arXiv:2608.30976v1 Announce Type: new Abstract: Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate doma...

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llms agents

Hugging Face Trending Papers

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A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domain expertise, assess prediction plausibility, and...

llms agents

arXiv AI

Jul 28

SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents

arXiv:2607. 24588v1 Announce Type: new Abstract: Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events.

By Hang Ni, Weijia Zhang, Fan Liu, Mengqian Lu, Hao Liu

llms agents efficiency benchmarks

arXiv AI

Sep 23

Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale

arXiv:2609.22702v1 Announce Type: new Abstract: Accurate flood forecasts several days in advance are essential for flood control, water-resource management, and emergency response. Producing them at...

By Hong Zhang, John K. Hutchison, Rao Kotamarthi, Jeremy Feinstein, Haiwen Guan, Romit Maulik, Vijay P. Ramalingam, Jason Stock, Tom Wall

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arXiv Machine Learning

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AFDBench: A Reasoning-First AI Scientist for NationalWeather Service Forecast Discussions

AFDBench is a new benchmark that evaluates how well large language models can generate professional Area Forecast Discussions (AFDs) for the National Weather Service by reasoning through structured AI weather forecast data. It contains 7,732 expert-written discussions paired with real forecast inputs and introduces three metrics—Met-Align, Style-Align, and Input-Grounding—to assess numerical accuracy, professional dialect adherence, and fidelity to source data. Zero-shot tests show open-source LLMs perform poorly on style and grounding, but reinforcement learning with Group Relative Policy Optimization nearly doubles style alignment and improves grounding, enabling a 7B-parameter model to write like a professional meteorologist.

By Manmeet Singh, Somnath Luitel, Prabhjot Singh, Manraaj Banga, Naveen Sudharsan, Josh Durkee

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AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

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Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale

AFDBench: A Reasoning-First AI Scientist for NationalWeather Service Forecast Discussions