activity
20192026
most citedTime Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources

29 citations · 47 across the 7 of their papers we have counts for

collaborators

7 papers

physics.geo-ph2026

Subsurface Property Mapping using Google AlphaEarth Foundations

Nori Nakata, Jingxiao Liu, Guodong Chen +2

Subsurface properties are essential for hazard assessment, energy and environmental management, and infrastructure resilience, but direct observations are sparse and uneven, motiva…

cs.LG2025

HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone

Yihan Wang, Annan Yu, Lujun Zhang +2

Recent advances have introduced diffusion models for probabilistic streamflow forecasting, demonstrating strong early flood-warning skill. However, current implementations rely on…

cs.LG2025

Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics

Yuan-Heng Wang, Yang Yang, Fabio Ciulla +2

While many modern studies are dedicated to ML-based large-sample hydrologic modeling, these efforts have not necessarily translated into predictive improvements that are grounded i…

cs.LG2024★ 2 cited

Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models

Jared D. Willard, Fabio Ciulla, Helen Weierbach +2

The prediction of streamflows and other environmental variables in unmonitored basins is a grand challenge in hydrology. Recent machine learning (ML) models can harness vast datase…

cs.LG2023★ 29 cited

Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources

Jared D. Willard, Charuleka Varadharajan, Xiaowei Jia +1

Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources h…

cs.LG2022★ 9 cited

Long-Term Missing Value Imputation for Time Series Data Using Deep Neural Networks

Jangho Park, Juliane Muller, Bhavna Arora +5

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron (MLP), for estimating the missing values of a variable in multivariate time series da…

stat.ML2019★ 7 cited

Surrogate Optimization of Deep Neural Networks for Groundwater Predictions

Juliane Mueller, Jangho Park, Reetik Sahu +4

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic mu…