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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “hydrometeorology”

Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasetsstat.ME

New framework uses time series features for predicting streamflow in ungauged areas.

problem Predicting streamflow in areas without gauging stations.

method Developed regression-based streamflow regionalization using a wide range of time series features from large datasets.

result Certain time series features, like entropy and autocorrelation, are better predictors of streamflow than traditional catchment attributes.

streamflow regionalization time series features ungauged areas regression models hydrometeorology

2022-04-13abs ↗pdf ↗

Probabilistic water demand forecasting using quantile regression algorithmsstat.ML

Quantile regression improves urban water demand forecasting.

problem Improving probabilistic urban water demand forecasting.

method Comparing five quantile regression algorithms and their combinations for one-day ahead forecasting.

result Linear boosting algorithm performs best for probabilistic urban water demand forecasting.

water demand quantile regression forecasting urban machine learning

2021-04-16abs ↗pdf ↗

Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessmentstat.ML

A new model predicts spatially varying inland flooding from time-varying inputs.

problem Ignoring time series and spatial correlations in flood models leads to inaccurate predictions.

method Introduced a multioutput Gaussian process model with separable kernels for functional inputs and spatial locations.

result The model provides accurate predictions of spatially varying inland flooding with minimal computational time.

Gaussian processes functional data spatial correlations coastal flood prediction time-varying inputs

2020-07-28abs ↗pdf ↗

Transformer-based Diffusion models for Hydrological Time Series Probabilistic Imputation and Forecastingstat.ML

Transformer-based diffusion models improve hydrological time series imputation and forecasting.

problem Limited observations in hydrometeorological time series.

method Transformer-based diffusion models applied to hydrological data.

result Transformer-based models efficiently sample realistic time series distributions under variable missing data.

hydrological time series diffusion models transformers imputation forecasting

2026-07-23abs ↗pdf ↗